Neurophysiological data analysis using spatiotemporal parcellation

ABSTRACT

A method of analyzing neurophysiological data recorded from a subject is disclosed. The method comprises identifying activity-related features in the data, and parceling the data according to the activity-related features to define a plurality of capsules, each representing a spatiotemporal activity region in the brain. The method further comprises comparing at least some of the defined capsules to at least one reference capsule, and estimating a brain function of the subject based on the comparison.

RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 61/725,614, filed Nov. 13, 2012, and U.S.Provisional Patent Application No. 61/760,101, filed Feb. 3, 2013, thecontents of which are incorporated herein by reference in theirentirety.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates toneurophysiology and, more particularly, but not exclusively, to methodand system for analyzing data using spatiotemporal parcellation.

Little is known about the mechanisms that allow the brain to selectivelyimprove the neural representations of behaviorally important stimuliwhile ignoring irrelevant stimuli. The brain is a complex structure ofnerve cells that produce signals called action potentials. These actionpotentials move from one cell to another across a gap called thesynapse. These potentials summate in the cortex and extend through thecoverings of the brain to the scalp, where they can be measured usingappropriate electrodes. Rhythmical measured activity representspostsynaptic cortical neuronal potentials which are synchronized by thecomplex interaction of large populations of cortical cells.

Behavioral functions are based upon flow among various functionalregions in the brain, involving specific spatiotemporal flow patterns. Aspecific spatiotemporal pattern underlying a certain behavioral functionis composed of functional brain regions, which are often active for atleast several tens of milliseconds and more. The flow of activity amongthose regions is often synchronization-based.

Known in the art are methods that identify discrete participatingregions for the purpose of relating behavioral functions to theirunderlying localized brain activities. Other techniques employ analysisof the flow from one region to another.

U.S. Pat. No. 6,792,304 discloses a method and a system for masscommunication assessment. A cognitive task is transmitted from a centralcontrol site to a plurality of remote test sites via Internet. The brainresponse of the subjects at the remote sites in response to the task isrecorded and transmitted back to the central control site via theInternet. The central control site then computes the variations in thebrain activities for the subjects at each of the selected sites.

U.S. Published Application No. 20040059241 discloses a method forclassifying and treating physiologic brain imbalances. Neurophysiologictechniques are used for obtaining a set of analytic brain signals from asubject, and a set of digital parameters is determined from the signals.The digital parameters are quantitatively mapped to various therapyresponsivity profiles. The signals and parameters for a subject arecompared to aggregate neurophysiologic information contained indatabases relating to asymptomatic and symptomatic referencepopulations, and the comparison is used for making treatmentrecommendations. Treatment response patterns are correlated as adependent variable to provide a connection to successful outcomes forclinical treatment of afflicted subjects.

International Publication No. WO 2007/138579, the contents of which arehereby incorporated by reference, describes a method for establishing aknowledge base of neuropsychological flow patterns. Signals frommultiple research groups for a particular behavioral process areobtained, and sources of activity participating in the particularbehavioral functions are localized. Thereafter, sets of patterns ofbrain activity are identified, and neuropsychological analysis isemployed for analyzing the localized sources and the identifiedpatterns. The analysis includes identification and ranking of possiblepathways. A set of flow patterns is then created and used as a knowledgebase. The knowledge base is then used as a constraint for reducing thenumber of ranked pathways.

International Publication Nos. WO 2009/069134, WO 2009/069135 and WO2009/069136, the contents of which are hereby incorporated by reference,describe a technique in which neurophysiological data are collectedbefore and after the subject has performed a task and/or action thatforms a stimulus. The stimulus is used for defining features in thedata, and the data are decomposed according to the defined features.Thereafter, the features are analyzed to determine one or more patternsin the data. The decomposition can employ clustering for locating one ormore important features in the data, wherein a collection of clustersforms an activity network. The data patterns can be analyzed fordefining a neural model which can be used for simulating the effect of aparticular pathology and/or treatment on the brain.

International Publication Nos. WO 2011/086563, the contents of which arehereby incorporated by reference, discloses analysis ofneurophysiological data, which includes identifying activity-relatedfeatures in the data, constructing a brain network activity (BNA)pattern having a plurality of nodes, each representing a feature of theactivity-related features, and assigning a connectivity weight to eachpair of nodes in the BNA pattern.

Additional background art includes U.S. Published Application No.20050177058, which discloses a system in which EEG readings from morethan one subject at the same or different locations are collected,analyzed and compared, when they are exposed to a common set of stimuli.The compatibility of the subjects is studied using their EEG readings,and concealed information is discovered or verified from.

SUMMARY OF THE INVENTION

According to an aspect of some embodiments of the present inventionthere is provided a method of analyzing neurophysiological data recordedfrom a brain of a subject. The method being executed by a data processorand comprises: identifying activity-related features in the data;parceling the data according to the activity-related features to definea plurality of capsules, each representing a spatiotemporal activityregion in the brain; comparing at least some of the defined capsules toat least one reference capsule; and estimating a brain function of thesubject based on the comparison.

According to some embodiments of the invention the comparison comprisescalculating, for each of the at least some of the defined capsules, astatistical score of a spatiotemporal vector corresponding to thecapsule using multidimensional statistical distribution describing arespective database capsule.

According to some embodiments of the invention each entry of thedatabase is also associated with a weight, and the method furthercomprises weighing the statistical score using the weight.

According to some embodiments of the invention the method comprisescalculating a correlation between the capsule and a respective databasecapsule.

According to some embodiments of the invention the comparison isexecuted irrespective of any inter-capsule relation.

According to some embodiments of the invention the inter-capsulerelation comprises at least one of spatial proximity between two definedcapsules, temporal proximity between two defined capsules, spectralproximity between two defined capsules, and energetic proximity betweentwo defined capsules.

According to some embodiments of the invention the method comprisesdetermining inter-capsule relations among the capsules, and constructinga capsule network pattern responsively to the inter-capsule relations,wherein the database to comprises database capsule network patterns, andwhere the comparison comprises comparing the constructed pattern to thedatabase pattern.

According to some embodiments of the invention the at least onereference capsule comprises an annotated database capsule stored in adatabase having a plurality of entries, and the method further comprisesaccessing the database.

According to some embodiments of the invention the at least onereference capsule comprises a baseline capsule defined usingneurophysiological data acquired from the same subject at a differenttime, and the method comprises comparing the variation of the capsulerelative to the baseline capsule, to a previously stored variation of afirst capsule annotated as normal and a second capsule also annotated asnormal.

According to some embodiments of the invention the at least onereference capsule comprises a baseline capsule defined usingneurophysiological data acquired from the same subject at a differenttime.

According to some embodiments of the invention the method comprisescomparing the variation of the capsule relative to the baseline capsule,to a previously stored variation of a first capsule annotated as normaland a second capsule also annotated as normal.

According to some embodiments of the invention the at least onereference capsule comprises a capsule defined using neurophysiologicaldata acquired form a different subject.

According to some embodiments of the invention the at least onereference capsule comprises a capsule defined using neurophysiologicaldata acquired form a different subject.

According to some embodiments of the invention the method comprises:constructing a brain network activity (BNA) pattern having a pluralityof nodes, each representing a feature of the activity-related features;assigning a connectivity weight to each pair of nodes in the BNApattern; comparing the constructed BNA to at least one reference BNApattern; wherein the estimation of the a brain function of the subjectis also based on the comparison to the reference BNA.

According to some embodiments of the invention the at least onereference BNA pattern comprises an annotated BNA pattern stored in a BNAdatabase having a plurality of entries, and the method further comprisesaccessing the database.

According to some embodiments of the invention the at least onereference BNA pattern comprises a baseline BNA pattern extracted fromneurophysiological data acquired from the same subject at a differenttime.

According to some embodiments of the invention the at least onereference BNA pattern comprises a BNA pattern extracted fromneurophysiological data acquired from a different subject or a group ofsubjects.

According to some embodiments of the invention the method comprises,prior to the comparison, applying a feature selection procedure to theplurality of capsules to provide at least one sub-set of capsules,wherein the comparison is executed separately for each of the at leastone sub-set of capsules.

According to some embodiments of the invention the brain function is atemporary abnormal brain function.

According to some embodiments of the invention the brain function is achronic abnormal brain function.

According to some embodiments of the invention the brain function is aresponse to a stimulus or lack thereof.

According to some embodiments of the invention the method comprisesassessing the likelihood of brain concussion.

According to some embodiments of the invention the method comprisesapplying local stimulation to the brain responsively to the estimatedbrain function, the local stimulation being at one or more locationscorresponding to a spatial location of at least one of the capsules.

According to some embodiments of the invention the method comprisesapplying local stimulation to the brain responsively to the estimatedbrain function.

According to some embodiments of the invention the local stimulation isat one or more locations corresponding to a spatial location of at leastone of the capsules.

According to some embodiments of the invention the estimation of thebrain function is executed repeatedly, and the method comprises varyingthe local stimulation responsively to variations in the brain function.

According to some embodiments of the invention the local stimulationcomprises transcranial stimulation.

According to some embodiments of the invention the local stimulationcomprises transcranial electrical stimulation (tES).

According to some embodiments of the invention the local stimulationcomprises transcranial direct current stimulation (tDCS).

According to some embodiments of the invention the local stimulationcomprises high-definition transcranial direct current stimulation(HD-tDCS).

According to some embodiments of the invention the local stimulationcomprises electrocortical stimulation on the cortex.

According to some embodiments of the invention the local stimulationcomprises deep brain stimulation.

According to some embodiments of the invention the local stimulationcomprises both transcranial stimulation and deep brain stimulation, andwherein the transcranial stimulation is executed to control activationthresholds for the deep brain stimulation.

According to some embodiments of the invention the local stimulationcomprises both transcranial stimulation and deep brain stimulation, andwherein the transcranial stimulation is executed to control activationthresholds for the deep brain stimulation.

According to an aspect of some embodiments of the present inventionthere is provided a method of constructing a database fromneurophysiological data recorded from a group of subjects. The methodbeing executed by a data processor and comprises: identifyingactivity-related features in the data; parceling the data according tothe activity-related features to define a plurality of capsules, eachrepresenting a spatiotemporal activity region in the brain; clusteringthe data according to the capsules, to provide a plurality of capsuleclusters; and storing the clusters and/or representations thereof in acomputer readable medium, thereby forming the database.

According to some embodiments of the invention the representations ofthe clusters comprises capsular representations of the clusters.

According to some embodiments of the invention the method according toany further comprising determining inter-capsule relations among thecapsules, and constructing capsule network patterns responsively to theinter-capsule relations, wherein the representations of the clusterscomprise the capsule network patterns.

According to some embodiments of the invention the parceling comprisesto forming a spatial grid, associating each identified activity-relatedfeature with a grid element and a time point, and defining a capsulecorresponding to the identified activity-related feature as aspatiotemporal activity region encapsulating grid elements nearby theassociated grid element and time points nearby the associated timepoints.

According to some embodiments of the invention the grid elements nearbythe associated grid element comprise all grid elements at which anamplitude level of the activity-related feature is within apredetermined threshold range.

According to some embodiments of the invention the time points nearbythe associated time point comprise all time points at which an amplitudelevel of the activity-related feature is within a predeterminedthreshold range.

According to some embodiments of the invention the spatial grid is atwo-dimensional spatial grid.

According to some embodiments of the invention the spatial grid is atwo-dimensional spatial grid describing a scalp of the subject.

According to some embodiments of the invention the spatial grid is atwo-dimensional spatial grid describing an intracranial surface of thesubject.

According to some embodiments of the invention the spatial grid is athree-dimensional spatial grid.

According to some embodiments of the invention the spatial grid is athree-dimensional spatial grid describing an intracranial volume of thesubject.

According to some embodiments of the invention the parceling comprisesapplying frequency decomposition to the data to provide a plurality offrequency bands, wherein the association of the identifiedactivity-related feature and the definition of the capsule is executedseparately for each frequency band.

According to some embodiments of the invention the parceling comprisesapplying frequency decomposition to the data to provide a plurality offrequency bands, wherein the association of the identifiedactivity-related feature and the definition of the capsule is executedseparately for each frequency band.

According to some embodiments of the invention the parceling comprisesassociating each identified activity-related feature with a frequencyvalue, and wherein the capsule corresponding to the identifiedactivity-related feature is defined as spectral-spatiotemporal activityregion encapsulating grid elements nearby the associated grid element,time points nearby the associated time points and frequency valuesnearby the associated frequency value.

According to an aspect of some embodiments of the present inventionthere is provided a system for processing neurophysiological data,comprising a data processor configured for receiving theneurophysiological data, and executing the method as delineated aboveand optionally as further exemplified below.

According to an aspect of some embodiments of the present inventionthere is provided a computer software product, comprising acomputer-readable medium in which program instructions are stored, whichinstructions, when read by a data processor, cause the data processor toreceive the neurophysiological data and execute the method as delineatedabove and optionally as further exemplified below.

According to an aspect of some embodiments of the present inventionthere is provided a system for analyzing neurophysiological datarecorded from a brain of a subject. The system comprises a dataprocessor configured for: identifying activity-related features in thedata; parceling the data according to the activity-related features todefine a plurality of capsules, each representing a spatiotemporalactivity region in the brain; comparing at least some of the definedcapsules to at least one reference capsule; and estimating a brainfunction of the subject based on the comparison.

According to some embodiments of the invention the system furthercomprises a controller connectable to a brain stimulation system andconfigured for controlling the brain stimulation system to apply localstimulation to the brain responsively to the estimated brain function.

According to some embodiments of the invention the controller isconfigured to control the brain stimulation system to apply the localstimulation at one or more locations corresponding to a spatial locationof at least one of the capsules.

According to some embodiments of the invention the estimation of thebrain function is executed repeatedly, and the controller is configuredto vary the local stimulation responsively to variations in the brainfunction.

According to some embodiments of the invention the brain stimulationsystem comprises a transcranial stimulation system.

According to some embodiments of the invention the brain stimulationsystem to comprises a transcranial direct current stimulation (tDCS)system.

According to some embodiments of the invention the local stimulationcomprises high-definition transcranial direct current stimulation(HD-tDCS).

According to some embodiments of the invention the brain stimulationsystem comprises an electrocortical stimulation system configured toapply electrocortical stimulation on the cortex.

According to some embodiments of the invention the brain stimulationsystem comprises a deep brain stimulation system.

According to some embodiments of the invention the brain stimulationsystem is configured to apply both transcranial stimulation and deepbrain stimulation, and wherein the controller is configured to controlthe brain stimulation system to apply the transcranial stimulation tocontrol activation thresholds for the deep brain stimulation.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

Implementation of the method and/or system of embodiments of theinvention can involve performing or completing selected tasks manually,automatically, or a combination thereof. Moreover, according to actualinstrumentation and equipment of embodiments of the method and/or systemof the invention, several selected tasks could be implemented byhardware, by software or by firmware or by a combination thereof usingan operating system.

For example, hardware for performing selected tasks according toembodiments of the invention could be implemented as a chip or acircuit. As software, selected tasks according to embodiments of theinvention could be implemented as a plurality of software instructionsbeing executed by a computer using any suitable operating system. In anexemplary embodiment of the invention, one or more tasks according toexemplary embodiments of method and/or system as described herein areperformed by a data processor, such as a computing platform forexecuting a plurality of instructions. Optionally, the data processorincludes a volatile memory for storing instructions and/or data and/or anon-volatile storage, for example, a magnetic hard-disk and/or removablemedia, for storing instructions and/or data. Optionally, a networkconnection is provided as well. A display and/or a user input devicesuch as a keyboard or mouse are optionally provided as well.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theparticulars shown are by way of example and for purposes of illustrativediscussion of embodiments of the invention. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the invention may be practiced.

In the drawings:

FIG. 1 is a flowchart diagram of a method suitable for analyzingneurophysiological data, according to various exemplary embodiments ofthe present invention.

FIG. 2 is a schematic illustration showing a representative example of aBrain Network Activity (BNA) pattern which can be extracted fromneurophysiological data, according to some embodiments of the presentinvention.

FIG. 3A is a flowchart diagram describing a procedure for identifyingactivity-related features for a group of subjects, according to someembodiments of the present invention.

FIG. 3B is schematic illustration of a procedure for determiningrelations between brain activity features, according to some embodimentsof the present invention;

FIGS. 3C-E are abstract illustrations of a BNA patterns constructedaccording to some embodiments of the present invention using theprocedure illustrated in FIG. 3B;

FIG. 4 is a flowchart diagram describing a method suitable for analyzinga subject-specific BNA pattern, according to various exemplaryembodiments of the present invention.

FIGS. 5A-F are schematic illustrations showing a representative examplefor a to process for determining a brain-disorder index, according tosome embodiments of the present invention.

FIGS. 6A-F are schematic illustrations showing representative examplesfor a process for assessing the responsiveness of an ADHD subject totreatment, according to some embodiments of the present invention.

FIGS. 7A-D are schematic illustrations showing representative examplesfor a process for assessing the responsiveness of another ADHD subjectto treatment, according to some embodiments of the present invention.

FIGS. 8A-E are schematic illustrations showing a representative examplefor a process for assessing the responsiveness of a subject toscopolamine, according to some embodiments of the present invention.

FIGS. 9A-B are schematic illustrations showing a representative examplefor use of the BNA pattern for measuring pain, according to someembodiments of the present invention.

FIGS. 10A-H are schematic illustrations of BNA patterns constructedaccording to some embodiments of the present invention from EEG datarecorded during a working memory test.

FIG. 11 shows graphical presentation of a brain-disorder index accordingto some embodiments of the present invention.

FIG. 12 shows results of a methylphenidate (MPH) study performedaccording to some embodiments of the present invention.

FIG. 13 shows evolutions of group BNA patterns of untreated ADHDsubjects (left column), ADHD subjects following treatment with MPH(middle column), and control (right column).

FIG. 14 is a flowchart diagram illustrating a method suitable forconstructing a database from neurophysiological data recorded from agroup of subjects, according to some embodiments of the presentinvention.

FIG. 15 is a flowchart diagram illustrating a method suitable foranalyzing neurophysiological data recorded from a subject, according tosome embodiments of the present invention.

FIG. 16 is a block diagram of a data analysis technique executed in anexperiment performed according to some embodiments of the presentinvention.

FIGS. 17A and 17B show Groups' capsules as obtained in an experimentperformed according to some embodiments of the present invention.

FIG. 18 shows A band ROC curves as obtained in an experiment performedaccording to some embodiments of the present invention.

FIG. 19 is a block diagram describing a technique utilized in anexemplified study performed according to some embodiments of the presentinvention.

FIG. 20 is a scheme illustrating a method employed during an exemplifiedstudy performed in accordance with some embodiments of the presentinvention.

FIG. 21 is a schematic representation of an Auditory Oddball Taskemployed in an exemplified study performed in accordance with someembodiments of the present invention.

FIG. 22 shows normative database's Interclass Correlation (ICC) valuesfor BNA scores in the two EEG-ERP sessions obtained during anexemplified study performed in accordance with some embodiments of thepresent invention.

FIG. 23 shows Q-Q plot for the Connectivity ΔBNA scores of a stimulusreferred to as “Novel stimulus” of an Auditory Oddball Task, as obtainedduring an exemplified study performed in accordance with someembodiments of the present invention.

FIG. 24 shows frequency histogram for Connectivity ΔBNA scores of astimulus referred to as “Novel stimulus” of an Auditory Oddball Task, asobtained during an exemplified study performed in accordance with someembodiments of the present invention.

FIG. 25 shows a reconstructed ERP at Fz channel of a randomly chosenhealthy subject from the normative database following a 6-step gradedmanipulation (combined amplitude decline and latency delay) of the P300component in response to a stimulus referred to as “Novel stimulus” ofan Auditory Oddball Task, as obtained during an exemplified studyperformed in accordance with some embodiments of the present invention.

FIGS. 26A-B show simulation results obtained during an exemplified studyperformed in accordance with some embodiments of the present invention.

FIG. 27 shows pharmacological model results obtained during anexemplified study performed in accordance with some embodiments of thepresent invention.

FIG. 28 is a block diagram describing a technique utilized in anexemplified experimental study performed according to some embodimentsof the present invention.

FIGS. 29A-B show selected reference BNA patterns for a Go/NoGo task(FIG. 29A), and an Auditory Oddball task (FIG. 29B), as obtained duringan exemplified experimental study performed according to someembodiments of the present invention.

FIGS. 30A-D show group average BNA scores (% similarity to the referenceBNA) across 4 visits for a concussed group (n=35) and a control group(n=19), as obtained during an exemplified experimental study performedaccording to some embodiments of the present invention.

FIGS. 31A-D shows sensitivity and specificity for BNA patterns, asobtained during an exemplified experimental study performed according tosome embodiments of the present invention.

FIG. 32 is a schematic illustration of a system for analyzingneurophysiological data, according to some embodiments of the presentinvention.

FIG. 33 is a schematic illustration of feature selection proceduresuitable for some embodiments of the present invention.

FIGS. 34A-C are schematic illustrations of comparison protocols suitablefor some embodiments of the present invention.

FIG. 35 shows one example of extracted spatiotemporal peaks in differentfrequency bands for a No-Go stimulus, used in experiments performedaccording to some embodiments of the present invention.

FIGS. 36A-C show results obtained during a feature selection experimentperformed according to some embodiments of the present invention.

FIG. 37 shows a visual analog scale (VAS) used in a study performedaccording to some embodiments of the present invention to investigatepain analysis and treatment.

FIG. 38 is a schematic illustration of an area at which heat stimuluswas applied in the study to investigate pain analysis and treatment.

FIG. 39 is a schematic illustration of a map of the electrodes that wereused in the study to investigate pain analysis and treatment.

FIG. 40 is a flowchart diagram describing a protocol used in the studyto investigate pain analysis and treatment.

FIG. 41 shows visual analog scale (VAS) as a function of the numericalpain scale, as obtained in the study to investigate pain analysis andtreatment.

FIG. 42 shows BNA score, VAS and the quality of life rating scale, asobtained in the study to investigate pain analysis and treatment.

FIG. 43 shows changes in the BNA scores, as predicted for the study toinvestigate pain analysis and treatment.

FIGS. 44A-D show representative Example of a subject declared asresponder the study to investigate pain analysis and treatment.

FIGS. 45A-C show representative Example of a subject declared asnon-responder the study to investigate pain analysis and treatment.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates toneurophysiology and, more particularly, but not exclusively, to methodand system for analyzing neurophysiological data.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings and/or the Examples. The invention iscapable of other embodiments or of being practiced or carried out invarious ways.

Embodiments of the present invention are directed to a tool which can beused for an individual subject or a group of subjects, to analyze theirbrain activity so as to extract information pertaining to, e.g.,behavior, condition, brain function, and other subject characteristics.The information is extracted by constructing one or more data objectsthat express the information. In some embodiments of the presentinvention the data object is a neurophysiological data pattern, in someembodiments the data object is a brain network activity (BNA) pattern,in some embodiments the data object is a spatiotemporal activity regionin the brain, and in some embodiments the data object is a network ofspatiotemporal activity regions.

The data object can aid both for diagnostics and for therapy fortreating pathologies associated with the respective data object. Asubject or group of subject can be analyzed in terms of one or moretypes of data objects. When the subject or group of subjects areanalyzed in terms of two or more data objects, the extracted informationfrom each object can be combined and/or weighed to formulate an estimateregarding the behavior, condition and/or brain function.

For example, the subject or group of subjects can first be analyzed byconstructing a BNA pattern to provide a first analysis, and also beanalyzed using one or more spatiotemporal activity regions to provide asecond analysis. The first and second analyses can be combined toprovide better assessment regarding the behavior, condition, brainfunction and/or other subject characteristics.

As a representative example one of the analyses can serve for confirmingassessments made by the other analysis. As another example, when each ofthe analyses includes a numerical assessment value (e.g., the likelihoodthat a particular subject has some disorder) the numerical assessmentvalues can be combined (e.g., by calculating an averaged or weightedaverage).

It is to be understood that, unless otherwise defined, the operationsdescribed hereinbelow can be executed either contemporaneously orsequentially in many combinations or orders of execution. Specifically,the ordering of the flowchart diagrams is not to be considered aslimiting. For example, two or more operations, appearing in thefollowing description or in the flowchart diagrams in a particularorder, can be executed in a different order (e.g., a reverse order) orsubstantially contemporaneously. Additionally, several operationsdescribed below are optional and may not be executed.

At least part of the operations can be can be implemented by a dataprocessing system, e.g., a dedicated circuitry or a general purposecomputer, configured for receiving the data and executing the operationsdescribed below.

Computer programs implementing the method of the present embodiments cancommonly be distributed to users on a distribution medium such as, butnot limited to, a floppy disk, a CD-ROM, a flash memory device and aportable hard drive. From the distribution medium, the computer programscan be copied to a hard disk or a similar to intermediate storagemedium. The computer programs can be run by loading the computerinstructions either from their distribution medium or their intermediatestorage medium into the execution memory of the computer, configuringthe computer to act in accordance with the method of this invention. Allthese operations are well-known to those skilled in the art of computersystems.

The method of the present embodiments can be embodied in many forms. Forexample, it can be embodied in on a tangible medium such as a computerfor performing the method operations. It can be embodied on a computerreadable medium, comprising computer readable instructions for carryingout the method operations. In can also be embodied in electronic devicehaving digital computer capabilities arranged to run the computerprogram on the tangible medium or execute the instruction on a computerreadable medium.

FIG. 1 is a flowchart diagram of a method suitable for analyzingneurophysiological data, according to various exemplary embodiments ofthe present invention.

The neurophysiological data to be analyzed can be any data acquireddirectly from the brain of the subject under investigation. The dataacquired “directly” in the sense that it shows electrical, magnetic,chemical or structural features of the brain tissue itself. Theneurophysiological data can be data acquired directly from the brain ofa single subject or data acquired directly from multiple brains ofrespective multiple subjects (e.g., a research group), not necessarilysimultaneously.

Analysis of data from multiple brains can be done by performing theoperations described below separately for each portion of the data thatcorrespond to a single brain. Yet, some operations can be performedcollectively for more than one brain. Thus, unless explicitly stateotherwise, a reference to “subject” or “brain” in the singular form doesnot necessarily mean analysis of data of an individual subject. Areference to “subject” or “brain” in the singular form encompasses alsoanalysis of a data portion which corresponds to one out of severalsubjects, which analysis can be applied to other portions as well.

The data can be analyzed immediately after acquisition (“onlineanalysis”), or it can be recorded and stored and thereafter analyzed(“offline analysis”).

Representative example of neurophysiological data types suitable for thepresent to invention, including, without limitation,electroencephalogram (EEG) data, magnetoencephalography (MEG) data,computer-aided tomography (CAT) data, positron emission tomography (PET)data, magnetic resonance imaging (MRI) data, functional MRI (fMRI) data,ultrasound data, single photon emission computed tomography (SPECT)data, Brain Computer Interface (BCI) data, and data from neuroprosthesesat the neural level. Optionally, the data include combination of two ormore different types of data.

In various exemplary embodiments of the invention the neurophysiologicaldata are associated with signals collected using a plurality ofmeasuring devices respectively placed at a plurality of differentlocations on the scalp of the subject. In these embodiments, the datatype is preferably EEG or MEG data. The measuring devices can includeelectrodes, superconducting quantum interference devices (SQUIDs), andthe like. The portion of the data that is acquired at each such locationis also referred to as “channel.” In some embodiments, theneurophysiological data are associated with signals collected using aplurality of measuring devices placed in the brain tissue itself. Inthese embodiments, the data type is preferably invasive EEG data, alsoknown as electrocorticography (ECoG) data.

Optionally and preferably, the neurophysiological data is collected atleast before and after the subject has performed a task and/or action.In some embodiments of the present invention the neurophysiological datais collected at least before and after the subject has conceptualized atask and/or action but has not actually performed the task. Theseembodiments are useful when the subject is suffering from some type ofphysical and/or cognitive deficit that may prevent actual execution of atask and/or action, as for example may be seen in response to variousbrain injuries such as stroke. Nevertheless, these embodiments can beemployed for any subject, if desired.

Neurophysiological data which is associated with a task and/or action(whether actually performed or conceptualized) can be used as eventrelated measures, such as event related potentials (ERPs) or eventrelated fields (ERFs). The task and/or action (whether actuallyperformed or conceptualized) is preferably in response to a stimulus orstimuli, and the acquisition of data is synchronized with the stimulusto establish a timeline of the response and extract data featuresresponsively to this timeline. Typically, but not necessarily, the datacollection is on-going such that to neurophysiological data arecollected continuously before, during and after performance orconceptualization of the task and/or action.

Various types of tasks are contemplated, both lower-level andhigher-level cognitive tasks and/or actions. The task/action can besingle, serial or on-going. An example of an on-going lower-levelcognitive task/action includes, without limitation, watching a movie; anexample of a single lower-level cognitive task/action includes, withoutlimitation, providing an audible signal (e.g., a simple sound) to thesubject; and an example of a serial lower-level cognitive task/actionincludes, without limitation, playing an audible signal repeatedly. Itis appreciated that for a repetitive task the subject may eventually beconditioned and will pay less attention (a process known ashabituation), but there still will be a response from the brain. Anexample of a higher-level cognitive task/action includes, withoutlimitation, the so called “Go/NoGo task” in which the subject isrequested to push a button if a high pitch sound is heard, wherein if alow pitch sound is heard then the subject is not to push the button.This task is known in the art and is used in many cognitive studies.

Many protocols of stimuli and stimuli-responses are known in the art,all of which are contemplated by some embodiments of the presentinvention. Stimulus-response neuropsychological tests include, withoutlimitation, the Stroop task, the Wisconsin card sorting test, and thelike; stimulus-only based tests include, without limitation, mismatchnegativity, brain-stem-evoked response audiometry (BERA), and the like.Also contemplated are response-only based tests, such as, but notlimited to, saccade analysis, movement related potentials (MRP), N-backmemory tasks and other working memory tasks, the “serial seven” test(counting back from 100 in jumps of seven), the Posner attention tasksand the like.

It is to be understood that it is not intended to limit the scope of thepresent invention only to neurophysiological data associated withstimulus, task and/or action. Embodiments of the present invention canbe applied also to neurophysiological data describing spontaneous brainactivity. Also contemplated are embodiments in which theneurophysiological data are acquired during particular activities, butthe acquisition is not synchronized with a stimulus.

Referring now to FIG. 1, the method begins at 10 and optionally andpreferably continues to 11 at which the neurophysiological data arereceived. The data can be recorded directly from the subject or it canbe received from an external source, such as a computer readable memorymedium on which the data are stored.

The method continues to 12 at which relations between features of thedata are determined so as to identify activity-related features. Thiscan be done using any procedure known in the art. For example,procedures as described in International Publication Nos. WO2007/138579, WO 2009/069134, WO 2009/069135 and WO 2009/069136, thecontents of which are hereby incorporated by reference, can be employed.Broadly speaking, the extraction of activity-related features includesmultidimensional analysis of the data, wherein the data is analyzed toextract spatial and non-spatial characteristics of the data.

The spatial characteristics preferably describe the locations from whichthe respective data were acquired. For example, the spatialcharacteristics can include the locations of the measuring devices(e.g., electrode, SQUID) on the scalp of the subject.

Also contemplated are embodiments in which the spatial characteristicsestimate the locations within the brain tissue at which theneurophysiological data were generated. In these embodiments, a sourcelocalization procedure, which may include, for example, low resolutionelectromagnetic tomography (LORETA), is employed. A source localizationprocedure suitable for the present embodiments is described in theaforementioned international publications which are incorporated byreference. Other source localization procedure suitable for the presentembodiments are found in Greenblatt et al., 2005, “Local LinearEstimators for the Bioelectromagnetic Inverse Problem,” IEEE Trans.Signal Processing, 53(9):5430; Sekihara et al., “Adaptive SpatialFilters for Electromagnetic Brain Imaging (Series in BiomedicalEngineering),” Springer, 2008; and Sekihara et al., 2005, “Localizationbias and spatial resolution of adaptive and non-adaptive spatial filtersfor MEG source reconstruction,” Neurolmage 25:1056; the contents ofwhich are hereby incorporated by reference.

Additionally contemplated are embodiments in which the spatialcharacteristics estimate locations on the epicortical surface. In theseembodiments, data collected at locations on the scalp of the subject areprocessed so as to map the scalp potential distribution onto theepicortical surface. The technique for such mapping is known in the artand referred to in the literature as Cortical Potential Imaging (CPI) orCortical to Source Density (CSD). Mapping techniques suitable for thepresent embodiments are found in Kayser et al., 2006, “PrincipalComponents Analysis of Laplacian Waveforms as a Generic Method forIdentifying ERP Generator Patterns: I. Evaluation with Auditory OddballTasks,” Clinical Neurophysiology 117(2):348; Zhang et al., 2006, “ACortical Potential Imaging Study from Simultaneous Extra- andIntra-cranial Electrical Recordings by Means of the Finite ElementMethod,” Neuroimage, 31(4): 1513; Perrin et al., 1987, “Scalp CurrentDensity Mapping: Value and Estimation from Potential Data,” IEEEtransactions on biomedical engineering, BME-34(4):283; Fence et al.,2000, “Theory and Calculation of the Scalp Surface Laplacian,”www.csi.uoregon.edu/members/ferree/tutorials/SurfaceLaplacian; andBabiloni et al., 1997, “High resolution EEG: a new model-dependentspatial deblurring method using a realistically-shaped MR-constructedsubject's head model,” Electroencephalography and clinicalNeurophysiology 102:69.

In any of the above embodiments, the spatial characteristics can berepresented using a discrete or continuous spatial coordinate system, asdesired. When the coordinate system is discrete, it typicallycorresponds to the locations of the measuring devices (e.g., locationson the scalp, epicortical surface, cerebral cortex or deeper in thebrain). When the coordinate system is continuous, it preferablydescribes the approximate shape of the scalp or epicortical surface, orsome sampled version thereof. A sampled surface can be represented by apoint-cloud which is a set of points in a three-dimensional space, andwhich is sufficient for describing the topology of the surface. For acontinuous coordinate system, the spatial characteristics can beobtained by piecewise interpolation between the locations of themeasuring devices. The piecewise interpolation preferably utilizes asmooth analytical function or a set of smooth analytical functions overthe surface.

In some embodiments of the invention the non-spatial characteristics areobtained separately for each spatial characteristic. For example, thenon-spatial characteristics can be obtained separately for each channel.When the spatial characteristics are continuous, the non-spatialcharacteristics are preferably obtained for a set of discrete pointsover the continuum. Typically, this set of discrete points includes atleast the points used for the piecewise interpolation, but may alsoinclude other points to over the sampled version of the surface.

The non-spatial characteristics preferably include temporalcharacteristics, which are obtained by segmenting the data according tothe time of acquisition. The segmentation results in a plurality of datasegments each corresponding to an epoch over which the respective datasegment was acquired. The length of the epoch depends on the temporalresolution characterizing the type of neurophysiological data. Forexample, for EEG or MEG data, a typical epoch length is approximately1000 ms.

Other non-spatial characteristics can be obtained by data decomposingtechniques. In various exemplary embodiments of the invention thedecomposition is performed separately for each data segment of eachspatial characteristic. Thus, for a particular data channel,decomposition is applied, e.g., sequentially to each data segment ofthis particular channel (e.g., first to the segment that corresponds tothe first epoch, then to the segment that correspond to the second epochand so on). Such sequential decomposition is performed for otherchannels as well.

The neurophysiological data can be decomposed by identifying a patternof extrema (peaks, troughs, etc.) in the data, or, more preferably bymeans of waveform analysis, such as, but not limited to, waveletanalysis. In some embodiments of the present invention the extremumidentification is accompanied by a definition of a spatiotemporalneighborhood of the extremum. The neighborhood can be defined as aspatial region (two- or three-dimensional) in which the extremum islocated and/or a time-interval during which the extremum occurs.Preferably, both a spatial region and time-interval are defined, so asto associate a spatiotemporal neighborhood for each extremum. Theadvantage of defining such neighborhoods is that they provideinformation regarding the spreading structure of the data over timeand/or space. The size of the neighborhood (in terms of the respectivedimension) can be determined based on the property of the extremum. Forexample, in some embodiments, the size of the neighborhood equals thefull width at half maximum (FWHM) of the extremum. Other definitions ofthe neighborhood are not excluded from the scope of the presentinvention.

The waveform analysis is preferably accompanied by filtering (e.g.,bandpass filtering) such that the wave is decomposed to a plurality ofoverlapping sets of signal extrema (e.g., peaks) which together make upthe waveform. The filters themselves may optionally be overlapping.

When the neurophysiological data comprise EEG data, one or more of thefollowing frequency bands can be employed during the filtering: deltaband (typically from about 1 Hz to about 4 Hz), theta band (typicallyfrom about 3 to about 8 Hz), alpha band (typically from about 7 to about13 Hz), low beta band (typically from about 12 to about 18 Hz), betaband (typically from about 17 to about 23 Hz), and high beta band(typically from about 22 to about 30 Hz). Higher frequency bands, suchas, but not limited to, gamma band (typically from about 30 to about 80Hz), are also contemplated.

Following the waveform analysis, waveform characteristics, such as, butnot limited to, time (latency), frequency and optionally amplitude arepreferably extracted. These waveform characteristics are preferablyobtained as discrete values, thereby forming a vector whose componentsare the individual waveform characteristics. Use of discrete values isadvantageous since it reduces the amount of data for further analysis.Other reduction techniques, such as, but not limited to, statisticalnormalization (e.g., by means of standard score, or by employing anystatistical moment) are also contemplated. Normalization can be used forreducing noise and is also useful when the method is applied to dataacquired from more than one subject and/or when the interfaces betweenthe measuring device and the brain vary among different subjects oramong different locations for a single subject. For example, statisticalnormalization can be useful when there is non-uniform impedance matchingamong EEG electrodes.

The extraction of characteristics results in a plurality of vectors,each of which includes, as the components of the vector, the spatialcharacteristics (e.g., the location of the respective electrode or othermeasuring device), and one or more non-spatial characteristics asobtained from the segmentation and decomposition. Each of these vectorsis a feature of the data, and any pair of vectors whose characteristicsobey some relation (for example, causal relation wherein the two vectorsare consistent with flow of information from the location associatedwith one vector to the location associated with the other vector)constitutes two activity-related features.

The extracted vectors thus define a multidimensional space. For example,when the components include location, time and frequency, the vectorsdefine a three-dimensional space, and when the components includelocation, time, frequency and to amplitude, the vectors define afour-dimensional space. Higher number of dimensions is not excluded fromthe scope of the present invention.

When the analysis is applied to neurophysiological data of one subject,each feature of the data is represented as a point within themultidimensional space defined by the vectors, and each set ofactivity-related features is represented as a set of points such thatany point of the set is within a specific distance along the time axis(also referred to hereinbelow as “latency-difference”) from one or moreother points in the set.

When the analysis is applied to neurophysiological data acquired from agroup or sub-group of subjects, a feature of the data is preferablyrepresented as a cluster of discrete points in the aforementionedmultidimensional space. A cluster of points can also be defined when theanalysis is applied to neurophysiological data of a single subject. Inthese embodiments, vectors of waveform characteristics are extractedseparately for separate stimuli presented to the subject, therebydefining clusters of points within the multidimensional space, whereeach point within the cluster corresponds to a response to a stimulusapplied at a different time. The separate stimuli optionally andpreferably form a set of repetitive presentations of the same or similarstimulus, or a set of stimuli which are not necessarily identical butare of the same type (e.g., a set of not-necessarily identical visualstimuli). Use of different stimuli at different times is not excludedfrom the scope of the present invention.

Also contemplated are combinations of the above representations, whereindata are collected from a plurality of subjects and for one or more ofthe subjects, vectors of waveform characteristics are extractedseparately for time-separated stimuli (i.e., stimuli applied at separatetimes). In these embodiments, a cluster contains points that correspondto different subjects as well as points that correspond to a response toa separated stimulus. Consider, for example, a case in which data werecollected from 10 subjects, wherein each subject was presented with 5stimuli during data acquisition. In this case, the dataset includes5×10=50 data segment, each corresponding to a response of one subject toone stimulus. Thus, in a cluster within the multidimensional space mayinclude up to 5×10 points, each representing a vector of characteristicsextracted from one of the data segments.

Whether representing characteristics of a plurality of subjects and/orcharacteristics of a plurality of responses to stimuli presented to asingle subject the to width of a cluster along a given axis of the spacedescribes a size of an activity window for the corresponding datacharacteristic (time, frequency, etc). As a representative example,consider the width of a cluster along the time axis. Such width isoptionally and preferably used by the method to describe the latencyrange within which the event occurs across multiple subjects. Similarly,the width of a cluster along the frequency axis can be used fordescribing the frequency band indicating an occurrence of an eventoccurring across multiple subjects; the widths of a cluster along thelocation axes (e.g., two location axes for data corresponding to a 2Dlocation map, and three location axes for data corresponding to a 3Dlocation map) can be used to define a set of adjoining electrodes atwhich the event occurs across multiple subjects, and the width of acluster along the amplitude axis can be used to define an amplituderange indicating an occurrence of event across multiple subjects.

For a group or sub-group of subjects, activity-related features can beidentified as follows. A single cluster along the time axis ispreferably identified as representing a unitary event occurring within atime window defined, as stated, by the width of the cluster. This windowis optionally and preferably narrowed to exclude some outlier points,thereby redefining the latency range characterizing the respective datafeature. For a succession of clusters along the time axis, wherein eachcluster in the series has a width (along the time axis) within aparticular constraint, a pattern extraction procedure is preferablyimplemented for identifying those clusters which obey connectivityrelations thereamongst. Broadly speaking such procedure can search overthe clusters for pairs of clusters in which there are connectivityrelations between a sufficient number of points between the clusters.

The pattern extraction procedure can include any type of clusteringprocedures, including, without limitation, a density-based clusteringprocedure, a nearest-neighbor-based clustering procedure, and the like.A density-based clustering procedure suitable for the presentembodiments is described in Cao et al., 2006, “Density-based clusteringover an evolving data stream with noise,” Proceedings of the Sixth SIAMInternational Conference on Data Mining Bethesda, Md., p. 328-39. Anearest-neighbor clustering procedure suitable for the presentembodiments is described in [R. O. Duda, P. E. Hart and D. G. Stork,“Pattern Classification” (2nd Edition), A Wiley-IntersciencePublication, 2000]. When nearest-neighbor clustering procedure isemployed, clusters to are identified and thereafter gathered to formmeta-clusters based on spatiotemporal distances among the clusters. Themeta-clusters are, therefore, clusters of the identified clusters. Inthese embodiments, the meta-clusters are the features of the data, andactivity-related features are identified among the meta-clusters.

FIG. 3A is a flowchart diagram describing a procedure for identifyingactivity-related features for a group of subjects, according to someembodiments of the present invention. The procedure begins at 40 andcontinues to 41 at which isolated clusters are identified. The presentembodiments contemplate both subspace clustering, wherein clusters areidentified on a particular projection of the multidimensional space, andfull-space clustering wherein clusters are identified on the entiremultidimensional space. Subspace clustering is preferred from thestandpoint of computation time, and full-space clustering is preferredfrom the standpoint of features generality.

One representative example of subspace clustering includesidentification of clusters along the time axis, separately for eachpredetermined frequency band and each predetermined spatial location.The identification optionally and preferably features a movingtime-window with a fixed and predetermined window width. A typicalwindow width for EEG data is about 200 ms for the delta band. Arestriction on a minimal number of points in a cluster is optionallyapplied so as not to exclude small clusters from the analysis. Typicallycluster with less than X points, where X equals about 80% of thesubjects in the group, are excluded. The minimal number of points can beupdated during the procedure. Once an initial set of clusters isdefined, the width of the time window is preferably lowered.

Another representative example of subspace clustering includesidentification of clusters over a space-time subspace, preferablyseparately for each predetermined frequency band. In this embodiment,the extracted spatial characteristics are represented using a continuousspatial coordinate system, e.g., by piecewise interpolation between thelocations of the measuring devices, as further detailed hereinabove.Thus, each cluster is associated with a time window as well as a spatialregion, wherein the spatial region may or may not be centered at alocation of a measuring device. In some embodiments, at least onecluster is associated with a spatial region which is centered at alocation other than a location of a measuring device. The space-timesubspace is to typically three-dimensional with one temporal dimensionand two spatial dimensions, wherein each cluster is associated with atime-window and a two-dimensional spatial region over a surface whichmay correspond, e.g., to the shape of the scalp surface, the epicorticalsurface and the like. Also contemplated is a four-dimensional space-timespace wherein each cluster is associated with a time-window and athree-dimensional spatial region over a volume corresponding, at leastin part, to internal brain.

Another representative example of subspace clustering includesidentification of clusters over a frequency-space-time subspace. In thisembodiment, instead of searching for clusters separately for eachpredetermined frequency band, the method allows identification ofclusters also at frequencies which are not predetermined. Thus, thefrequency is considered as a continuous coordinate over the subspace. Asin the embodiment of space-time subspace, the extracted spatialcharacteristics are represented using a continuous spatial coordinatesystem. Thus, each cluster is associated with a time window, a spatialregion and a frequency band. The spatial region can be two- orthree-dimensional as further detailed hereinabove. In some embodiments,at least one cluster is associated with a spatial region which iscentered at a location other than a location of a measuring device, andat least one cluster is associated with a frequency band which includesfrequencies of two or more of the delta, theta, alpha, low beta, beta,high beta and gamma bands. For example, a cluster can be associated witha frequency band spanning over part of the delta band and part of thetheta band, or part of the theta band and part of the alpha band, orpart of the alpha band and part of the low beta band, etc.

The procedure optionally and preferably continues to 42 at which, a pairof clusters is selected. The procedure optionally and preferablycontinues to 43 at which, for each subject that is represented in theselected pair, latency difference (including zero difference) betweenthe corresponding events is optionally calculated. The procedurecontinues to 44 at which a constraint is applied to the calculatedlatency differences such that latency differences which are outside apredetermined threshold range (e.g., 0-30 ms) are rejected while latencydifferences which are within the predetermined threshold range areaccepted. The procedure continues to decision 45 at which the proceduredetermines whether the number of accepted differences is sufficientlylarge (i.e., above to some number, e.g., above 80% of the subjects inthe group). If the number of accepted differences is not sufficientlylarge the procedure proceeds to 46 at which the procedure accepts thepair of clusters and identifies it as a pair of activity-relatedfeatures. If the number of accepted differences is sufficiently largethe procedure proceeds to 47 at which the procedure reject the pair.From 46 or 47 the procedure of the present embodiments loops back to 42.

An illustrative example for determining relations among the datafeatures and identification of activity-related features is shown inFIG. 3B. The illustration is provided in terms of a projection onto atwo-dimensional space which includes time and location. The presentexample is for an embodiment in which the spatial characteristics arediscrete, wherein the identification of clusters is along the time axis,separately for each predetermined frequency band and each predeterminedspatial location. The skilled person would know how to adapt thedescription for the other dimensions, e.g., frequency, amplitude, etc.FIG. 3B illustrates a scenario in which data are collected from 6subjects (or from a single subject, present with 6 stimuli at differenttimes), enumerated 1 through 6. For clarity of presentation, differentdata segments data (e.g., data collected from different subjects, orfrom the same subject but for stimuli of different times) are separatedalong a vertical axis denoted “Data Segment No.” For each segment, anopen circle represents an event recorded at one particular location (bymeans of a measuring device, e.g., EEG electrode) denoted “A”, and asolid disk represents an event recorded at another particular locationdenoted “B”.

The time axis represents the latency of the respective event, asmeasured, e.g., from a time at which the subject was presented with astimulus. The latencies of the events are denoted herein t^((i)) _(A)and t^((i)) _(B), where i represents the segment index (i=1, . . . , 6)and A and B represent the location. For clarity of presentation, thelatencies are not shown in FIG. 3B, but one of ordinary skills in theart, provided with the details described herein would know how to addthe latencies to the drawing.

For each of locations A and B, a time window is defined. These timewindows, denoted Δt_(A) and Δt_(B), correspond to the width of theclusters along the time axis and they can be the same or different fromone another, as desired. Also defined is a latency difference windowΔt_(AB), between the two unitary events. This window corresponds to theseparation along the time axis between the clusters (e.g., between theircenters). The window Δt_(AB) is illustrated as an interval having adashed segment and a solid segment. The length of the dashed segmentrepresents the lower bound of the window and the overall length of theinterval represents the upper bound of the window. Δt_(A), Δt_(B) andΔt_(AB) are part of the criteria for determining whether to accept thepair of events recorded at A and B as activity-related features.

The time windows Δt_(A) and Δt_(B) are preferably used for identifyingunitary events in the group. As shown, for each of segment Nos. 1, 2, 4and 5 both events fall within the respective time windows(mathematically, this can be written as follows: t^((i)) _(A)∈Δt_(A),t^((i)) _(B)∈Δt_(A), i=1, 2, 4, 5). On the other hand, for segment No. 3the event recorded at A falls outside Δt_(A) (t⁽³⁾ _(A)∉Δt_(A)) whilethe event recoded at B falls within Δt_(B) (t⁽³⁾ _(B)∈Δt_(B)), and forsegment No. 6 the event recorded at A falls within Δt_(A) (t⁽⁶⁾_(A)∈Δt_(A)) while the event recoded at B falls outside Δt_(B) (t⁽⁶⁾_(B)∉Δt_(B)). Thus, for location A, a unitary event is defined as acluster of data points obtained from segment Nos. 1, 2, 4, 5, and 6, andfor location B, a unitary event is defined as a cluster of data pointsobtained from segment Nos. 1-5.

The latency difference window Δt_(AB) is preferably used for identifyingactivity-related features. In various exemplary embodiments of theinvention the latency difference Δt^((i)) _(AB) (i=1, 2, . . . , 5) ofeach segment is compared to the latency difference window Δt_(AB). Invarious exemplary embodiments of the invention a pair of features isaccepted as an activity-related pair if (i) each of the features in thepair belongs to a unitary event, and (ii) the corresponding latencydifference falls within Δt_(AB). In the illustration of FIG. 3B, each ofthe pairs recorded from segment Nos. 4 and 5 is accepted as a pair ofactivity-related features, since both criteria are met for each of thosesegment (Δt^((i)) _(AB) ∈Δt_(AB), t^((i)) _(A)∈Δt_(A), t^((i))_(B)∈Δt_(A), i=4, 5). The pairs recorded from segment Nos. 1-3 do notpass the latency difference criterion since each of Δt⁽¹⁾ _(AB), Δt⁽²⁾_(AB) and Δt⁽³⁾ _(AB) is outside Δt_(AB) (Δt^((i)) _(AB)∉Δt_(AB), i=1,2, 3). These pairs are, therefore, rejected. Notice that in the presentembodiment, even though the pair obtained from segment No. 6 passes thelatency difference criterion, the pair is rejected since it fails topass the time-window criterion (Δt⁽⁶⁾ _(AB)∉Δt_(AB)).

In various exemplary embodiments of the invention the procedure alsoaccepts pairs corresponding to simultaneous events of the data thatoccur at two or more different locations. Although such events are notcausal with respect to each other (since there is no flow of informationbetween the locations), the corresponding features are marked by themethod. Without being bounded to any particular theory, the presentinventors consider that simultaneous events of the data are causallyrelated to another event, although not identified by the method. Forexample, the same physical stimulus can generate simultaneous events intwo or more locations in the brain.

The identified pairs of activity-related features, as accepted at 46,can be treated as elementary patterns which can be used as elementarybuilding blocks for constructing complex patterns within the featurespace. In various exemplary embodiments of the invention, the methodproceeds to 48 at which two or more pairs of activity-related featuresare joined (e.g., concatenated) to form a pattern of more than twofeatures. The criterion for the concatenation can be similarity betweenthe characteristics of the pairs, as manifested by the vectors. Forexample, in some embodiments, two pairs of activity-related features areconcatenated if they have a common feature. Symbolically, this can beformulated as follows: the pairs “A-B” and “B-C” have “B” as a commonfeature and are concatenated to form a complex pattern A-B-C.

Preferably, the concatenated set of features is subjected to athresholding procedure, for example, when X % or more of the subjects inthe group are included in the concatenated set, the set is accepted, andwhen less than X % of the subjects in the group are included in theconcatenated set, the set is rejected. A typical value for the thresholdX is about 80.

Each pattern of three or more features thus corresponds to a collectionof clusters defined such that any cluster of the collection is within aspecific latency-difference from one or more other clusters in thecollection. Once all pairs of clusters are analyzed the procedurescontinues to terminator 49 at which it ends.

Referring again to FIG. 1, at 13 a brain network activity (BNA) patternis constructed.

The concept of BNA pattern can be better understood with reference toFIG. 2 which is a representative example of a BNA pattern 20 which maybe extracted from neurophysiological data, according to some embodimentsof the present invention. BNA pattern 20 has a plurality of nodes 22,each representing one of the activity-related features. For example, anode can represent a particular frequency band (optionally two to ormore particular frequency bands) at a particular location and within aparticular time-window or latency range, optionally with a particularrange of amplitudes.

Some of nodes 22 are connected by edges 24 each representing the causalrelation between the nodes at the ends of the respective edge. Thus, theBNA pattern is a represented as a graph having nodes and edges. Invarious exemplary embodiments of the invention the BNA pattern includesplurality of discrete nodes, wherein information pertaining to featuresof the data is represented only by the nodes and information pertainingto relations among the features is represented only by the edges.

FIG. 2 illustrates BNA pattern 20 within a template 26 of a scalp,allowing relating the location of the nodes to the various lobes of thebrain (frontal 28, central 30, parietal 32, occipital 34 and temporal36). The nodes in the BNA pattern can be labeled by their variouscharacteristics. A color coding or shape coding visualization techniquecan also be employed, if desired. For example, nodes corresponding to aparticular frequency band can be displayed using one color or shape andnodes corresponding to another frequency band can be displayed usinganother color or shape. In the representative example of FIG. 2, twocolors are presented. Red nodes correspond to Delta waves and greennodes correspond to Theta waves.

BNA pattern 20 can describe brain activity of a single subject or agroup or sub-group of subjects. A BNA pattern which describes the brainactivity of a single subject is referred to herein as a subject-specificBNA pattern, and BNA pattern which describes the brain activity of agroup or sub-group of subjects is referred to herein as a group BNApattern.

When BNA pattern 20 is a subject-specific BNA pattern, only vectorsextracted from data of the respective subject are used to construct theBNA pattern. Thus, each node corresponds to a point in themultidimensional space and therefore represents an activity event in thebrain. When BNA pattern 20 is a group BNA pattern, some nodes cancorrespond to a cluster of points in the multidimensional space andtherefore represents an activity event which is prevalent in the groupor sub-group of subjects. Due to the statistical nature of a group BNApattern, the number of nodes (referred to herein as the “order”) and/oredges (referred to herein as the “size”) in a group BNA pattern istypically, but not necessarily, larger than the order and/or size of asubject-specific BNA pattern.

As a simple example for constructing a group BNA pattern, the simplifiedscenario illustrated in FIG. 3B is considered, wherein a “segment”corresponds to a different subject in a group or sub-group of subjects.The group data include, in the present example, two unitary eventsassociated with locations A and B. Each of these events forms a clusterin the multidimensional space. In various exemplary embodiments of theinvention each of the clusters, referred to herein as clusters A and B,is represented by a node in the group BNA. The two clusters A and B areidentified as activity-related features since there are some individualpoints within these clusters that pass the criteria for such relation(the pairs of Subject Nos. 4 and 5, in the present example). Thus, invarious exemplary embodiments of the invention the nodes correspondingto clusters A and B are connected by an edge. A simplified illustrationof the resulting group BNA pattern is illustrated in FIG. 3C.

A subject-specific BNA pattern is optionally and preferably constructedby comparing the features and relations among features of the datacollected from the respective subject to the features and relationsamong features of reference data, which, in some embodiments of thepresent invention comprise group data. In these embodiments, points andrelations among points associated with the subject's data are comparedto clusters and relations among clusters associated with the group'sdata. Consider, for example, the simplified scenario illustrated in FIG.3B, wherein a “segment” corresponds to a different subject in a group orsub-group of subjects. Cluster A does not include a contribution fromSubject No. 3, and cluster B does not include a contribution fromSubject No. 6, since for these subjects the respective points fail topass the time-window criterion. Thus, in various exemplary embodimentsof the invention when a subject-specific BNA pattern is constructed forSubject No. 3 it does not include a node corresponding to location A,and when a subject-specific BNA pattern is constructed for Subject No. 6it does not include a node corresponding to location B. On the otherhand, both locations A and B are represented as nodes in thesubject-specific BNA patterns constructed for any of Subject Nos. 1, 2,4 and 5.

For those subjects for which the respective points are accepted as apair of activity-related features (Subject Nos. 4 and 5, in the presentexample), the corresponding nodes are preferably connected by an edge. Asimplified illustration of a to subject-specific BNA pattern for such acase is shown in FIG. 3D.

Note that for this simplified example of only two nodes, thesubject-specific BNA of FIG. 3D is similar to the group BNA of FIG. 3C.For a larger number of nodes, the order and/or size of the group BNApattern is, as stated, typically larger than the order and/or size ofthe subject-specific BNA pattern. An additional difference between thesubject-specific and group BNA patterns can be manifested by the degreeof relation between the activity-related features represented by theedges, as further detailed hereinbelow.

For subjects for which the respective points were rejected (Subject Nos.1 and 2, in the present example), the corresponding nodes are preferablynot connected by an edge. A simplified illustration of asubject-specific BNA pattern for such case is shown in FIG. 3E.

It is to be understood, however, that although the above technique forconstructing a subject-specific BNA pattern is described in terms of therelation between the data of a particular subject to the data of a groupof subjects, this need not necessarily be the case, since in someembodiments, a subject-specific BNA pattern can be constructed only fromthe data of a single subject. In these embodiments, vectors of waveformcharacteristics are extracted separately for time-separated stimuli, todefine clusters of points where each point within the clustercorresponds to a response to a stimulus applied at a different time, asfurther detailed hereinabove. The procedure for constructingsubject-specific BNA pattern in these embodiments is preferably the sameas procedure for constructing a group BNA pattern described above.However, since all data are collected from a single subject, the BNApattern is subject-specific.

Thus, the present embodiments contemplate two types of subject-specificBNA patterns: a first type that describes the association of theparticular subject to a group or sub-group of subjects, which is amanifestation of a group BNA pattern for the specific subject, and asecond type that describes the data of the particular subject withoutassociating the subject to a group or sub-group of subjects. The formertype of BNA pattern is referred to herein as an associatedsubject-specific BNA pattern, and the latter type of BNA pattern isreferred to herein as an unassociated subject-specific BNA pattern.

For unassociated subject-specific BNA pattern, the analysis ispreferably performed on the set of repetitive presentations of a singlestimulus, namely on a set of single trials, optionally and preferablybefore averaging the data and turning it to one single vector of thedata. For group BNA patterns, on the other hand, the data of eachsubject of the group is optionally and preferably averaged andthereafter turned into vectors of the data.

Note that while the unassociated subject-specific BNA pattern isgenerally unique for a particular subject (at the time thesubject-specific BNA pattern is constructed), the same subject may becharacterized by more than one associated subject-specific BNA patterns,since a subject may have different associations to different groups.Consider for example a group of healthy subjects and a group ofnon-healthy subjects all suffering from the same brain disorder.Consider further a subject Y which may or may not belong to one of thosegroups. The present embodiments contemplate several subject-specific BNApatterns for subject Y. A first BNA pattern is an unassociatedsubject-specific BNA pattern, which, as stated is generally unique forthis subject, since it is constructed from data collected only fromsubject Y. A second BNA pattern is an associated subject-specific BNApattern constructed in terms of the relation between the data of asubject Y to the data of the healthy group. A third BNA pattern is anassociated subject-specific BNA pattern constructed in terms of therelation between the data of a subject Y to the data of the non-healthygroup. Each of these BNA patterns are useful for assessing the conditionof subject Y. The first BNA pattern can be useful, for example, formonitoring changes in the brain function of the subject over time (e.g.,monitoring brain plasticity or the like) since it allows comparing theBNA pattern to a previously constructed unassociated subject-specificBNA pattern. The second and third BNA pattern can be useful fordetermining the level of association between subject Y and therespective group, thereby determining the likelihood of brain disorderfor the subject.

Also contemplated are embodiments in which the reference data used forconstructing the subject-specific BNA pattern corresponds to historydata previously acquired from the same subject. These embodiments aresimilar to the embodiments described above regarding the associatedsubject-specific BNA pattern, except that the BNA pattern is associatedto the history of the same subject instead of to a group of subjects.

Additionally contemplated are embodiments in which the reference datacorresponds to data acquired from the same subject at some later time.These embodiments allow investigating whether data acquired at an earlytime evolve into the data acquired at the later time. A particular andnon limiting example is the case of several treatment sessions, e.g., Nsessions, for the same subject. Data acquired in the first severaltreatment sessions (e.g., from session 1 to session k₁<N) can be used asreference data for constructing a first associated subject-specific BNApattern corresponding to mid sessions (e.g., from session k₂>k₁ tosession k₃>k₂), and data acquired in the last several treatment sessions(e.g., from session k₄ to session N) can be used as reference data forconstructing a second associated subject-specific BNA patterncorresponding to the aforementioned mid sessions, where 1<k₁<k₂<k₃<k₄.Such two associated subject-specific BNA patterns for the same subjectcan be used for determining data evolution from the early stages of thetreatment to the late stages of the treatment.

The method proceeds to 14 at which a connectivity weight is assigned toeach pair of nodes in the BNA pattern (or, equivalently, to each edge inthe BNA) pattern, thereby providing a weighted BNA pattern. Theconnectivity weight is represented in FIGS. 2, 3C and 3D by thethickness of the edges connecting two nodes. For example, thicker edgescan correspond to higher weights and thinner edges can correspond tolower weights.

In various exemplary embodiments of the invention the connectivityweight comprises a weight index WI calculated based on at least one ofthe following cluster properties: (i) the number of subjectsparticipating in the corresponding cluster pair, wherein greater weightsare assigned for larger number of subjects; (ii) the difference betweenthe number of subjects in each cluster of the pair (referred to as the“differentiation level” of the pair), wherein greater weights areassigned for lower differentiation levels; (iii) the width of the timewindows associated with each of the corresponding clusters (see, e.g.,Δt_(A) and Δt_(B) in FIG. 3A), wherein greater weights are assigned fornarrower windows; (iv) the latency difference between the two clusters(see Δt_(AB) in FIG. 3A), wherein greater weights are assigned fornarrower windows; (v) the amplitude of the signal associated with thecorresponding clusters; (vi) the frequency of the signal associated withthe corresponding clusters; and (vii) the width of a spatial windowdefining the cluster (in embodiments in which the coordinate system iscontinuous). For any of the cluster properties, except properties (i)and (ii), one or more statistical observables of the property, such as,but not limited to, average, median, supremum, infimum and variance overthe cluster are preferably used.

For a group BNA pattern or an unassociated subject-specific BNA pattern,the connectivity weight preferably equals the weight index WI ascalculated based on the cluster properties.

For an associated subject-specific BNA pattern, the connectivity weightof a pair of nodes is preferably assigned based on the weight index WIas well as one or more subject-specific and pair-specific quantitiesdenoted SI. Representative examples of such quantities are providedbelow.

In various exemplary embodiments of the invention a pair of nodes of theassociated subject-specific BNA pattern is assigned with a connectivityweight which is calculated by combining WI with SI. For example, theconnectivity weight of a pair in the associated subject-specific BNApattern can be given by WI·SI. When more than one quantities (say Nquantities) are calculated for a given pair of nodes, the pair can beassigned with more than one connectivity weights, e.g., WI·SI₁, WI·SI₂,. . . , WI·SI_(N), wherein SI₁, SI₂, . . . , SI_(N), are N calculatedquantities. Alternatively or additionally, all connectivity weights of agiven pair can be combined, e.g., by averaging, multiplying and thelike.

The quantity SI can be, for example, a statistical score characterizingthe relation between the subject-specific pair and the correspondingclusters. The statistical score can be of any type, including, withoutlimitation, deviation from average, absolute deviation, standard-scoreand the like. The relation for whom the statistical score is calculatedcan pertain to one or more properties used for calculating the weightindex WI, including, without limitation, latency, latency difference,amplitude, frequency and the like.

A statistical score pertaining to latency or latency difference isreferred to herein as a synchronization score and denoted SIs. Thus, asynchronization score according to some embodiments of the presentinvention can be obtained by calculating a statistical score for (i) thelatency of the point as obtained for the subject (e.g., t^((i)) _(A) andt^((i)) _(B), in the above example) relative to the group-averagelatency of the corresponding cluster, to and/or (ii) the latencydifference between two points as obtained for the subject (e.g.,Δt^((i)) _(AB)), relative to the group-average latency differencebetween the two corresponding clusters.

A statistical score pertaining to amplitude is referred to herein as anamplitude score and denoted SIa. Thus an amplitude score according tosome embodiments of the present invention is obtained by calculating astatistical score for the amplitude as obtained for the subject relativeto the group-average amplitude of the corresponding cluster.

A statistical score pertaining to frequency is referred to herein as afrequency score and denoted SIf. Thus a frequency score according tosome embodiments of the present invention is obtained by calculating astatistical score for the frequency as obtained for the subject relativeto the group-average frequency of the corresponding cluster.

A statistical score pertaining to the location is referred to herein asa location score and denoted SIl. These embodiments are particularlyuseful in embodiments in which a continuous coordinate system isemployed, as further detailed hereinabove. Thus a location scoreaccording to some embodiments of the present invention is obtained bycalculating a statistical score for the location as obtained for thesubject relative to the group-average location of the correspondingcluster.

Calculation of statistical scores pertaining to other properties is notexcluded from the scope of the present invention.

Following is a description of a technique for calculating the quantitySI, according to some embodiments of the present invention.

When SI is a synchronization score SIs the calculation is optionally andpreferably based on the discrete time points matching the spatiotemporalconstraints set by the electrode pair (Time_(subj)), if such exist. Inthese embodiments, the times of these points can are compared to themean and standard deviation of the times of the discrete pointsparticipating in the group pattern (Time_(pat)), for each region toprovide a regional synchronization score SIs_(r). The synchronizationscore SIs can then be calculated, for example, by averaging the regionalsynchronization scores of the two regions in the pair. Formally, thisprocedure can be written as:

${{SIs}_{r} = {0.5 + \frac{{std}\left( {Time}_{pat} \right)}{2*\left( {{{abs}\left( {\overset{\_}{{Time}_{pat}} - {Time}_{subj}} \right)} + {{std}\left( {Time}_{pat} \right)}} \right)}}};$${SIs} = {\frac{1}{r}{\sum{SIs}_{r}}}$

An amplitude score SIa, is optionally and preferably calculated in asimilar manner. Initially the amplitude of the discrete points of theindividual subject (Amp_(subj)) is compared to the mean and standarddeviation of the amplitudes of the discrete points participating in thegroup pattern (Amp_(pat)), for each region to provide a regionalamplitude score SIa_(r). The amplitude score can then be calculated, forexample, by averaging the regional amplitude scores of the two regionsin the pair:

${{SIa}_{r} = {0.5 + \frac{{std}\left( {Amp}_{pat} \right)}{2*\left( {{{abs}\left( {\overset{\_}{{Amp}_{pat}} - {Amp}_{subj}} \right)} + {{std}\left( {Amp}_{pat} \right)}} \right)}}};$${SIa} = {\frac{1}{r}{\sum{SIa}_{r}}}$

One or more BNA pattern similarities S can then be calculated as aweighted average over the nodes of the BNA pattern, as follows:

${Ss} = \frac{\sum\limits_{i}\left( {W_{i}*{SIs}_{i}} \right)}{\sum\limits_{i}W_{i}}$${Sa} = \frac{\sum\limits_{i}\left( {W_{i}*{SIa}_{i}} \right)}{\sum\limits_{i}W_{i}}$${Sf} = \frac{\sum\limits_{i}\left( {W_{i}*{SIf}_{i}} \right)}{\sum\limits_{i}W_{i}}$${Sl} = \frac{\sum\limits_{i}\left( {W_{i}*{SIl}_{i}} \right)}{\sum\limits_{i}W_{i}}$

Formally, an additional similarity, Sc, can be calculated, as follows:

${{Ic} = \frac{\sum\limits_{i}\left( {W_{i}*{SIc}_{i}} \right)}{\sum\limits_{i}W_{i}}},$

where SIc_(i) is a binary quantity which equals 1 if pair i exists inthe subject's data and 0 otherwise.

In some embodiments of the present invention the quantity SI comprises acorrelation value between recorded activities. In some embodiments, thecorrelation value describes correlation between the activities recordedfor the specific subject at the two locations associated with the pair,and in some embodiments the correlation value to describes correlationbetween the activities recorded for the specific subject at any of thelocations associated with the pair and the group activities as recordedat the same location. In some embodiments, the correlation valuedescribes causality relations between activities.

Procedures for calculating correlation values, such as causalityrelations, are known in the art. In some embodiments of the presentinvention the Granger theory is employed [Granger C W J, 1969,“Investigating Causal Relations By Econometric Models And Cross-SpectralMethods,” Econometrica, 37(3):242]. Other techniques suitable for thepresent embodiments are found in Durka et al., 2001, “Time-frequencymicrostructure of event-related electroencephalogram desynchronisationand synchronisation,” Medical & Biological Engineering & Computing,39:315; Smith Bassett et al., 2006, “Small-World Brain Networks”Neuroscientist, 12:512; He et al., 2007, “Small-World AnatomicalNetworks in the Human Brain Revealed by Cortical Thickness from MRI,”Cerebral Cortex 17:2407; and De Vico Fallani et al., “ExtractingInformation from Cortical Connectivity Patterns Estimated from HighResolution EEG Recordings: A Theoretical Graph Approach,” Brain Topogr19:125; the contents of all of which are hereby incorporated byreference.

The connectivity weights assigned over the BNA pattern can be calculatedas a continuous variable (e.g., using a function having a continuousrange), or as a discrete variable (e.g., using a function having adiscrete range or using a lookup table). In any case, connectivityweights can have more than two possible values. Thus, according tovarious exemplary embodiments of the present invention the weighted BNApattern has at least three, or at least four, or at least five, or atleast six edges, each of which being assigned with a differentconnectivity weight.

In some embodiments of the present invention the method proceeds to 16at which a feature selection procedure is applied to the BNA pattern toprovide at least one sub-set of BNA pattern nodes.

Feature selection is a process by which the dimensionality of the datais reduced to by selecting the best features of the input variables froma large set of candidates that are most relevant for the learningprocess of an algorithm. By removing irrelevant data the accuracy ofrepresenting the original features of a data set is increased therebyenhancing the accuracy of data mining tasks such as predictive modeling.Existing feature selection methods fall into two broad categories knownas forward selection and backward selection. Backward selection (e.g.,Marill et al., IEEE Tran Inf Theory 1963, 9:11-17; Pudil et al.,Proceedings of the 12th International Conference on Pattern Recognition(1994). 279-283; and Pudil et al., Pattern Recognit Lett (1994)15:1119-1125) starts with all the variables and removes them one by onein a step-wise fashion to be left with the top-ranked variables. Forwardselection (e.g. Whitney et al., IEEE Trans Comput 197; 20:1100-1103;Benjamini et al., Gavrilov Ann Appl Stat 2009; 3:179-198) starts with anempty variable set and adds the best variable at each step until anyfurther addition does not improve the model.

In some embodiments of the present invention a forward selection offeatures is employed and in some embodiments of the present invention abackward selection features is employed. In some embodiments of thepresent invention the method employs a procedure for controlling thefraction of false positives that may lead to poor selection, suchprocedure is known as false discovery rate (FDR) procedure, and isfound, for example, in Benjamini et al. supra, the contents of which arehereby incorporated by reference.

A representative example of a feature selection procedure suitable forthe present embodiments is illustrated in FIG. 33. Initially, a group ofsubjects is considered (for example, either healthy controls or diseasedsubjects), optionally and preferably using a sufficiently large datasetto as to provide relatively high accuracy in representing the group. Thegroup can be represented using a BNA pattern. The feature selectionprocedure is then applied on a training set of the dataset in order toevaluate each feature characterizing the group's dataset, wherein theevaluated feature can be a node of the BNA pattern or a pair of nodes ofthe BNA pair pattern or any combinations of nodes of the BNA pattern.The input to the feature selection algorithm is preferably evaluationscores (e.g., the score for each participant in the training set on eachof the features) calculated using the training set. Feature selectioncan also be applied, on other features, such as, but not limited to, EEGand ERP features such as, but not limited to, coherence, to correlation,timing and amplitude measures. Feature selection can also be applied ondifferent combinations of these features.

The outcome of this procedure can be a set of supervised BNA patterns(denoted “supervised networks” in FIG. 33), each suitable to describe adifferent sub-group of the population with a specific set of features.The supervised BNA patterns obtained during the procedure can allow acomparison of the BNA pattern obtained for a single subject to aspecific network or networks. Thus, the supervised BNA patterns canserve as biomarkers.

Once the BNA pattern is constructed it can be transmitted to a displaydevice such as a computer monitor, or a printer. Alternatively oradditionally, the BNA pattern can be transmitted to a computer-readablemedium.

The method ends at 15.

FIG. 4 is a flowchart diagram describing a method suitable for analyzinga subject-specific BNA pattern, according to various exemplaryembodiments of the present invention. The method begins at 50 andcontinues to 51 at which a BNA pattern, more preferably a weighted BNApattern, of the subject is obtained, for example, by following theoperations described above with reference to FIGS. 1, 2 and 3. The BNApattern obtained at 51 is referred to below as BNA pattern 20. BNApattern 20 can be displayed on a display device such as a computermonitor, printed, and/or stored in a computer-readable medium, asdesired.

In various exemplary embodiments of the invention BNA pattern 20 is anassociated subject-specific BNA pattern, constructed based on relationsbetween the data of the subject to group data represented by apreviously annotated BNA pattern. The previously annotated BNA patterncan optionally and preferably be an entry in a database of previouslyannotated BNA patterns, in which case the method preferably obtains anassociated subject-specific BNA pattern for each BNA pattern of thedatabase.

The term “annotated BNA pattern” refers to a BNA pattern which isassociated with annotation information. The annotation information canbe stored separately from the BNA pattern (e.g., in a separate file on acomputer readable medium). The annotation information is preferablyglobal annotation wherein the entire BNA pattern is identified ascorresponding to a specific brain related disorder or condition. Thus,for to example, the annotation information can pertain to the presence,absence or level of the specific disorder, condition or brain function.Also contemplated are embodiments in which the annotation informationpertains to a specific brain related disorder or condition in relationto a treatment applied to the subject. For example, a BNA pattern can beannotated as corresponding to a treated brain related disorder. Such BNApattern can also be annotated with the characteristics of the treatment,including dosage, duration, and elapsed time following the treatment. ABNA pattern can optionally and preferably be annotated as correspondingto an untreated brain related disorder.

As used herein, the term “treatment” includes abrogating, substantiallyinhibiting, slowing or reversing the progression of a condition,substantially ameliorating clinical or aesthetical symptoms of acondition or substantially preventing the appearance of clinical oraesthetical symptoms of a condition. Treatment can include any type ofintervention, both invasive and noninvasive, including, withoutlimitation, pharmacological, surgical, irradiative, rehabilitative, andthe like.

Alternatively or additionally, the BNA pattern can be identified ascorresponding to a specific group of individuals (e.g., a specificgender, ethnic origin, age group, etc.), wherein the annotationinformation pertains to the characteristics of this group ofindividuals. In some embodiments of the present invention the annotationinformation includes local annotation wherein nodes at several locationsover the BNA pattern are identified as indicative of specific disorder,condition and/or group.

The method proceeds to 52 at which BNA pattern 20 is compared to thepreviously annotated BNA pattern. In embodiments in which severalsubject-specific BNA patterns are obtained for the same subject, each ofthe subject-specific BNA patterns are preferably compared to thecorresponding annotated BNA pattern. The method optionally andpreferably selects the pair of BNA patterns which best match each other.Optionally, the method can assign a score to each pair of BNA patternsbeing compared. Such score can be, for example, one or more BNA patternsimilarity S, as further detailed hereinabove. Thus, in variousexemplary embodiments of the invention 52 includes calculation of atleast one BNA pattern similarity S, describing the similarity betweenBNA pattern 20 and the previously annotated BNA pattern.

In various exemplary embodiments of the invention BNA pattern 20 iscompared to at least one BNA pattern annotated as abnormal, and at leastone BNA pattern annotated as normal. A BNA pattern annotated as abnormalis a BNA pattern which is associated with annotation informationpertaining to the presence, absence or level of a brain related disorderor condition. A BNA pattern annotated as normal is a BNA pattern whichwas extracted from a subject, or more preferably, a group of subjects,identified as having normal brain function. Comparison to a BNA patternannotated as abnormal and a BNA pattern annotated as normal is usefulfor classifying BNA pattern 20 according to the respective brain relateddisorder or condition. Such classification is optionally and preferablyprovided by means of likelihood values expressed using similaritiesbetween a subject-specific BNA pattern and a group BNA pattern.

Representative examples of brain related disorder or conditions to whicha subject-specific BNA pattern can be classified according to thepresent include, without limitation, attention deficit hyperactivitydisorder (ADHD), stroke, traumatic brain injury (TBI), mild TBI(commonly known as brain concussion), posttraumatic stress disorder(PTSD), pain (e.g., labor pain, acute pain, chronic pain, mechanicalpain, static allodynia, dynamic allodynia, bone cancer pain, headache,osteoarthritic pain, inflammatory pain, and pain associated withautoimmune disorders or fibromyalgia), epilepsy, Parkinson, multiplesclerosis, agitation, abuse, Alzheimer's disease/dementia, anxiety,panic, phobic disorder, bipolar disorder, borderline personalitydisorder, behavior control problems, body dysmorphic disorder, cognitiveproblems (e.g., mild cognitive impairment), depression, dissociativedisorders, eating disorder, appetite disorder, fatigue, hiccups,impulse-control problems, irritability, mood problems, movementproblems, obsessive-compulsive disorder, personality disorders,schizophrenia and other psychotic disorders, seasonal affectivedisorder, sexual disorders, sleep disorders, stuttering, substanceabuse, Tourette's Syndrome, Trichotillomania, orviolent/self-destructive behaviors.

The phrase “inflammatory pain” means pain due to edema or swelling ofany inflamed tissue, including inflammatory joint pain. Inflammatoryjoint pain includes rheumatoid arthritic pain.

The phrase “acute pain” means any pain, including, but not limited to,joint pain, osteoarthritic pain, rheumatoid arthritic pain, inflammatorypain, pain from a burn, pain from a cut, surgical pain, pain fromfibromyalgia, bone cancer pain, menstrual pain, back pain, headache,static allodynia, and dynamic allodynia, that lasts from 1 minute to to91 days, 1 minute to 31 days, 1 minute to 7 days, 1 minute to 5 days, 1minute to 3 days, 1 minute to 2 days, 1 hour to 91 days, 1 hour to 31days, 1 hour to 7 days, 1 hour to 5 days, 1 hour to 3 days, 1 hour to 2days, 1 hour to 24 hours, 1 hour to 12 hours, or 1 hour to 6 hours, peroccurrence if left untreated. Acute pain includes, but is not limitedto, joint pain, osteoarthritic pain, rheumatoid arthritic pain,inflammatory pain, pain from a burn, pain from a cut, surgical pain,pain from fibromyalgia, bone cancer pain, menstrual pain, back pain,headache, static allodynia, dynamic allodynia, acute joint pain, acuteosteoarthritic pain, acute rheumatoid arthritic pain, acute inflammatorypain, acute headache, acute menstrual pain, acute back pain, and acutepain from fibromyalgia. Acute pain may be selected from acute jointpain, acute osteoarthritic pain, acute rheumatoid arthritic pain, acuteinflammatory pain, acute headache, acute menstrual pain, and acute backpain. Acute pain may be selected from acute joint pain, acuteosteoarthritic pain, acute rheumatoid arthritic pain, and acuteinflammatory pain. Acute pain may be selected from acute joint pain,acute osteoarthritic pain, and acute rheumatoid arthritic pain. Acutepain may be selected from acute joint pain and acute osteoarthriticpain.

The previously annotated BNA pattern can optionally and preferably be abaseline annotated BNA pattern characterizing a group of subjectsidentified as having normal brain function or having the same braindisorder. Such baseline annotated BNA pattern is optionally larger thanBNA pattern 20 in terms of the order (namely the number of nodes in theBNA pattern) and and/or size of (namely the number of edges in the BNApattern). Representative examples of baseline BNA patterns andtechniques for constructing and annotating such baseline BNA patternsare described in the Examples section that follows.

The comparison between BNA patterns, according to some embodiments ofthe present invention is preferably quantitative. In these embodimentsthe comparison between the BNA patterns comprises calculating BNApattern similarity. The BNA pattern similarity is optionally andpreferably calculated based on the values of the connectivity weights ofthe BNA patterns. For example, BNA pattern similarity can be obtained byaveraging the connectivity weights over the subject-specific BNApattern. When more than one type of connectivity weight is assigned foreach pair of nodes in BNA pattern 20, the averaging is preferablyperformed over the BNA pattern separately for each type of connectivityweight. Optionally and preferably one or more of the averages can becombined (e.g., summed, multiplied, averaged, etc.) to provide acombined BNA pattern similarity. Alternatively, a representative of theaverages (e.g., the largest) can be defined as the BNA patternsimilarity.

The BNA pattern similarity can be used as a classification score whichdescribes, quantitatively, the membership level of the subject to therespective group. This embodiment is particularly useful when more thanone subject-specific BNA patterns are constructed for the same subjectusing different group data, wherein the classification score can be usedto assess the membership level of the subject to each of the groups.

The similarity can be expressed as a continuous or discrete variable. Invarious exemplary embodiments of the invention the similarity is anon-binary number. In other words, rather than determining whether thetwo BNA patterns are similar or dissimilar, the method calculates thedegree by which the two BNA patterns are similar or dissimilar. Forexample, the similarity can be expressed as percentage, as a non-integernumber between 0 and 1 (e.g., 0 corresponding to complete dissimilarityand 1 corresponding to comparison between a BNA pattern and itself), andthe like.

The above procedure for calculating the similarity can be performed bothfor the comparison between the subject-specific BNA pattern 20 and a BNApattern annotated as abnormal, and for the comparison between thesubject-specific BNA pattern 20 and a BNA pattern annotated as normal.

The comparison between the subject's BNA pattern and the reference BNApattern is optionally and preferably with respect to the supervised BNApatterns obtained during the feature selection procedure (see, forexample, FIG. 33).

Several comparison protocols are contemplated, and are schematicallyillustrated in FIGS. 34A-C. These comparison protocols are particularlyuseful to construct a single subject BNA pattern that can be used as abaseline against which the subject can be scored across multiple tests.The advantage of such baseline is that variability among data obtainedwithin the subject is typically smaller than the variability betweensubjects. Thus, according to some embodiments of the present inventionthe BNA to pattern of the subject is compared to a BNA pattern thatcorresponds to the same subject.

In the comparison illustrated in FIG. 34A, a matching process thatallows quantifying the degree of similarity between the brain activityof the single subject and that represented by the BNA pattern(s) of thegroup is employed. The overall degree of similarity can be quantified,according to some embodiments of the present invention, by a score whichis a weighted sum of the separated similarity scores associated with allof the compared features. In embodiments in which several BNA patternsare obtained, each BNA pattern characterizes a specific sub-group in thepopulation. In these embodiments, the subject can be matched against aBNA pattern or BNA patterns associated with a sub-group that mostresemble the characteristics of the subject.

In the comparison illustrated in FIG. 34B, the BNA pattern of thesubject is compared against the group BNA pattern and representativematching features (e.g., best matching features) of the single subjectto those of the group network are preferably selected. Theserepresentative matching features can be used as an approximation of theintersection between the single-subject BNA pattern and the group BNApattern and constitute a personalized single-subject BNA sub-patternthat serves as a reference baseline used in multiple tests of the samesubject.

In some embodiments, the single subject may be compared against severalgroup BNA sub-pattern describing homogeneous subtypes enablingfine-tuning in choosing a single subject BNA pattern that can serve as areference. Thus, matching individual features to the features of thegroup's BNA pattern allows the extraction of a customized BNA patternand a comparison of the individual to a sub-set of features mostcharacterizing their condition (e.g., healthy, diseased).

In the comparison illustrated in FIG. 34C, various combination ofcomparisons are shown. These include, but are not limited to, singlesubject BNA pattern against another single subject BNA pattern, BNApattern against the intersection between the BNA pattern and the singlesubject BNA pattern, and the like.

At 53 the method extracts information pertaining to the condition of thesubject, responsively to the comparison between BNA pattern 20 and theannotated BNA pattern(s). Once the information is extracted, it can betransmitted to a computer-readable medium or a display device or aprinting device, as desired. Many types of information are contemplatedby the present inventors. Representative examples of such types arefurther detailed hereinbelow.

The method ends at 54.

In various exemplary embodiments of the invention, the extractedinformation pertains to the likelihood of abnormal brain function forthe subject. Additionally, the BNA pattern comparison can optionally andpreferably be used for extracting prognostic information. For example,BNA pattern 20 can be compared to a baseline annotated BNA pattern thatcharacterizes a group of subject all suffering from the same abnormalbrain function with similar rehabilitation history, wherein the baselineannotated BNA pattern is constructed from neurophysiological dataacquired at the beginning of the rehabilitation process. The similaritylevel between BNA pattern 20 and that baseline annotated BNA pattern canbe used as a prognosis indicator for the particular abnormal brainfunction and the particular rehabilitation process.

The likelihood of abnormal brain function is optionally and preferablyextracted by determining a brain-disorder index based, at least in part,on the similarity between BNA pattern 20 and the annotated BNApattern(s). For example, when a similarity between BNA pattern 20 and aBNA pattern annotated as corresponding to ADHD is calculated, thesimilarity can be used for calculating an ADHD index. The brain-disorderindex can be the similarity itself or it can be calculated based on thesimilarity. In various exemplary embodiments of the invention thebrain-disorder index is calculated based on the similarity between BNApattern 20 and a BNA pattern annotated as abnormal, as well as thesimilarity between BNA pattern 20 and a BNA pattern annotated as normal.For example, denoting the former similarity by S_(abnormal) and thelatter similarity by S_(normal), where both S_(abnormal) and S_(normal)are between 0 and 1, the brain-disorder index I_(disorder) can becalculated as:

I _(disorder)=(S _(abnormal)+(1−S _(normal)))/2.

Variations of the above formula are not excluded from the scope of thepresent invention.

A representative example for a process for determining a brain-disorderindex for the case of an ADHD is illustrated in FIGS. 5A-F, showing BNApatterns constructed from EEG data. In FIGS. 5A-F, red nodes correspondto ERP at the Delta frequency band, green nodes correspond to ERP at theTheta frequency band, and yellow nodes correspond to ERP at the Alphafrequency band. The BNA patterns also include nodes corresponding tolocations where ERPs at more than one frequency band have been recorded.These nodes are shown as mixed colors. Specifically, green-red nodescorrespond to ERP at the Delta and Theta frequency bands, andyellow-green nodes correspond to ERP at the Alpha and Theta frequencybands.

FIG. 5A shows a baseline BNA pattern annotated as normal, and FIG. 5Dshows a baseline BNA pattern annotated as corresponding to ADHD. Each ofthese two BNA patterns was constructed from a group of adult subjectidentified as normal and having ADHD, respectively. As shown in FIG. 5Athe baseline BNA pattern for normal brain function has nodes thatrepresent ERPs, predominantly at the delta frequency band (red nodes),at a plurality of frontal-posterior locations at the right hemisphere.The characteristic time window of the delta nodes has a width of about50 ms. The characteristic latencies of the delta nodes are, on theaverage, about 90-110 ms and about 270-330 ms. As shown in FIG. 5D thebaseline BNA pattern for ADHD has nodes that represent ERPs,predominantly at the theta and alpha frequency bands (green and yellownodes), at a plurality of frontocentral locations. The BNA pattern forADHD may also include nodes in the central-parietal locations. Thecharacteristic time window Δt_(A) of the theta and alpha nodes is fromabout 100 ms to about 200 ms.

FIGS. 5B and 5E show associated subject-specific BNA patternsconstructed based on comparison to the normal and ADHD baseline groupBNA patterns, respectively. The similarity values, calculated asdescribed above, are S_(normal)=0.76 (FIG. 5B) and S_(ADHD)=0.47 (FIG.5E). Thus the BNA pattern of this subject is more similar to the normalbaseline BNA pattern than to the ADHD baseline BNA pattern. The ADHDindex of this subject can be set to 0.47, or, more preferably,(0.47+(1−0.76))/2=0.355.

FIGS. 5C and 5F show the results of a comparison between asubject-specific BNA pattern (constructed for another single subject) tothe normal and ADHD baseline BNA patterns, respectively. The similarityvalues, calculated as described above, are S_(normal)=0.32 (FIG. 5C) andS_(ADHD)=0.68 (FIG. 5F). Thus the BNA pattern of this subject is moresimilar to the ADHD baseline BNA pattern than to the normal baseline BNApattern, and the ADHD index of this subject can be set to 0.68, or, morepreferably, (0.68+(1−0.32))/2=0.68.

The brain-disorder index can be presented to the user graphically on ascale-bar. A representative example of such graphical presentation forthe case of ADHD is shown in FIG. 11.

While the embodiments above were described with a particular emphasis toADHD, it is to be understood that more detailed reference to thisdisorder is not to be interpreted as limiting the scope of the inventionin any way. Thus, the BNA pattern comparison technique can be used forassessing likelihood of many brain related disorders, including any ofthe aforementioned brain related disorders. Further examples regardingthe assessment of likelihood of brain related disorders are provided inthe Examples section that follows (see Example 1 for ADHD and Example 5for Mild Cognitive Impairment and Alzheimer's Disease).

A baseline annotated BNA pattern can also be associated with annotationinformation pertaining to a specific brain related disorder or conditionof a group of subjects in relation to a treatment applied to thesubjects in the group. Such baseline BNA pattern can also be annotatedwith the characteristics of the treatment, including dosage, duration,and elapsed time following the treatment. A comparison of BNA pattern 20to such type of baseline BNA patterns, can provide information regardingthe responsiveness of the subject to treatment and/or the efficiency ofthe treatment for that particular subject. Such comparison canoptionally and preferably be used for extracting prognostic informationin connection to the specific treatment. A BNA pattern that iscomplementary to such baseline BNA pattern is a BNA pattern that isannotated as corresponding to an untreated brain related disorder.

Optionally and preferably, the method compares BNA pattern 20 to atleast one baseline BNA pattern annotated as corresponding to a treatedbrain related disorder, and at least one baseline BNA pattern annotatedas corresponding to an untreated brain related disorder. Representativeexamples for a process for assessing the responsiveness of a subject totreatment using such two baseline BNA patterns is illustrated in FIGS.6A-F, 7A-D and 8A-E.

The BNA patterns shown in FIGS. 6A-D are associated subject-specific BNApatterns constructed from EEG data recorded from a particular ADHDsubject. The black dots in FIGS. 6A-D show the locations of the EEGelectrodes. The color codes in to these BNA patterns are the same asdefined above. The subject-specific BNA patterns shown in FIGS. 6A-Bdescribe the association of the ADHD subject to a group of untreatedADHD subjects, and the BNA patterns shown in FIGS. 6C-D describe theassociation of the ADHD subject to a group of ADHD subjects all treatedwith methylphenidate (MPH). The subject-specific BNA patterns shown inFIGS. 6A and 6C are based on EEG data recorded from the ADHD subjectbefore any treatment, and subject-specific BNA patterns shown in FIGS.6B and 6D are based on EEG data recorded from the ADHD subject followinga treatment with MPH.

The baseline annotated BNA pattern constructed from the group ofuntreated ADHD subjects, and the baseline annotated BNA patternconstructed from the same group of subjects, but following treatmentwith MPH are shown in FIGS. 6E and 6F, respectively.

A BNA pattern similarity was calculated for each of the subject-specificBNA patterns shown in FIGS. 6A-D. The calculated similaritycorresponding to the BNA pattern of FIG. 6A is 0.73, the calculatedsimilarity corresponding to the BNA pattern of FIG. 6B is 0.19, thecalculated similarity corresponding to the BNA pattern of FIG. 6C is0.56, and the calculated similarity corresponding to the BNA pattern ofFIG. 6D is 0.6. It is recognized by the present inventors that thesesimilarity values indicate that the subject is responsive to thetreatment. Before treatment, the subject's BNA pattern had a relativelyhigh similarity (0.73) to the baseline BNA pattern for the group ofuntreated ADHD subjects and a relatively low similarity (0.56) to thebaseline BNA pattern for the group of treated ADHD subjects, meaningthat this subject can be classified with that the group of untreatedADHD subjects. Following a single dose treatment with MPH, thesimilarity value to the baseline BNA pattern for untreated ADHD groupwas scientifically reduced from 0.73 to 0.19, while the similarity valueto the baseline BNA pattern for the treated ADHD group was increasedfrom 0.56 to 0.6, meaning that after treatment a single dose, thesubject's brain activity no longer has the characteristics of untreatedADHD activity, but rather has the characteristics of treated ADHDactivity.

Some results of the MPH study for ADHD subjects are summarized in FIG.12. For each subject, two associated subject-specific BNA patterns wereconstructed. A first BNA pattern described the association of thesubject to a group of untreated ADHD subjects, and a second BNA patterndescribed the association of the subject to a group of to healthysubjects (control). The left bar shows average score for subjects beforetreatment with MPH, the middle bar shows average score for subjectsafter treatment with MPH, and the rightmost bar shows the score of thecontrol group.

A representative example of the evolution of the group BNA patterns overtime is shown in FIG. 13. Shown in FIG. 13 are three columns of BNApatterns, corresponding to the groups of untreated ADHD subjects (leftcolumn), ADHD subjects following treatment with MPH (middle column), andcontrol (right column). The evolution is shown at intervals of 50 ms.The topmost BNA pattern at each column is formed by a superposition ofthe other patterns in that column

Further details regarding analysis of neurophysiological data acquiredfrom ADHD subjects are provided in the Examples section that follows(see Example 1).

The BNA pattern technique of the present embodiments can also be usedfor determining a recommended dose for the subject. Specifically, thedose can be varied until a sufficiently high or maximal similarity tothe baseline BNA pattern for treated subjects is obtained. Once suchsimilarity is achieved, the method can determine that the dose achievingsuch similarity is the recommended dose for this subject.

The BNA patterns shown in FIGS. 7A-D were constructed from EEG datarecorded from a different ADHD subject, which was also treated with MPHaccording to the same protocol as described above with respect to theresponder subject of FIGS. 6A-D. The black dots in FIGS. 7A-D show thelocations of the EEG electrodes, and the color codes in these BNApatterns is the same as defined above. Thus, the subject-specific BNApatterns shown in FIGS. 7A-B describe the association of the ADHDsubject to a group of untreated ADHD subjects, and the BNA patternsshown in FIGS. 7C-D describe the association of the ADHD subject to agroup of ADHD subjects all treated with methylphenidate (MPH). Thesubject-specific BNA patterns shown in FIGS. 7A and 7C are based on EEGdata recorded from the ADHD subject before any treatment, andsubject-specific BNA patterns shown in FIGS. 7B and 7D are based on EEGdata recorded from the ADHD subject following a treatment with MPH.

Note that the BNA patterns of FIGS. 7A and 7D do not include any nodesand edges. This, however, does not mean that the subjects had no brainactivity. A void associated subject-specific BNA pattern means that noneof data features of the respective subject was member of a cluster inthe group to which the subject is attempted to be associated with.

A BNA pattern similarity was calculated for each of the subject-specificBNA patterns shown in FIGS. 7A-D. The calculated similaritycorresponding to the BNA pattern of FIG. 7A is 0, the calculatedsimilarity corresponding to the BNA pattern of FIG. 7B is 0, thecalculated similarity corresponding to the BNA pattern of FIG. 7C is0.06, and the calculated similarity corresponding to the BNA pattern ofFIG. 7D is 0. It is recognized by the present inventors that thesesimilarity values indicate that the subject is not responsive to thetreatment.

FIGS. 8A-D show associated subject-specific BNA patterns constructedfrom EEG data recorded from two healthy volunteer subjects. The blackdots in FIGS. 8A-D show the locations of the EEG electrodes, and thecolor codes in these BNA patterns are the same as defined above. Thesubject-specific BNA patterns shown in FIGS. 8A-D describe theassociation of the subjects to a group of healthy subjects followingtreatment with a placebo drug and while performing an attention taskrelated oddball task. The baseline annotated BNA pattern of this groupis shown in FIG. 8E.

FIGS. 8A and 8C are subject-specific BNA patterns constructed from EEGdata collected from a first subject (FIG. 8A) and a second subject (FIG.8C) following treatment with a placebo, and FIGS. 8B and 8D aresubject-specific BNA patterns constructed from EEG data collected fromthe first subject (FIG. 8B) and the second subject (FIG. 8D) followingtreatment with a scopolamine drug. Scopolamine is an anticholinergicdrug with inhibitory effect on M2-cholinergic receptors of excited type.It has an inhibitory effect on the cerebral cortex, typically inducingslight-anesthetic effect.

A BNA pattern similarity was calculated for each of the subject-specificBNA patterns shown in FIGS. 8A-D. The calculated similarities are 0.937,0.079, 1.0 and 0.94, respectively. It is recognized by the presentinventors that these similarity values indicate that the responsivity toscopolamine is high for the first subject (FIGS. 8A and 8B) and low forthe second subject (FIGS. 8C and 8D). These conclusions were alsoconfirmed in clinical observations wherein, following treatment with thescopolamine, a 70% decrease in behavioral endpoint was observed for thefirst subject, but no change in behavioral endpoint was observed for thesecond subject.

Further details regarding analysis of neurophysiological data acquiredfrom to subjects administered with scopolamine are provided in theExamples section that follows (see Example 4).

The above examples demonstrate that the BNA pattern comparison techniqueof the present embodiments can be used for quantitative assessment ofthe responsivity to treatment. While the embodiments above weredescribed with a particular emphasis to treatments with MPH andscopolamine, it is to be understood that more detailed reference tothese treatments is not to be interpreted as limiting the scope of theinvention in any way. Thus, the BNA pattern comparison technique can beused for assessing responsiveness to and efficacy of many types oftreatments.

In various exemplary embodiments of the invention, the extractedinformation pertains to the level of pain the subject is experiencing.Preferably, the information includes an objective pain level. Pain levelassessment according to some embodiments of the present invention isparticularly useful in institutions that provide treatment orrehabilitation for subjects suffering from chronic pain. Arepresentative example for the use of BNA pattern for measuring pain isillustrated in FIGS. 9A and 9B, showing BNA patterns constructed fromEEG data during a pain study which is further detailed in the Examplessections that follows (see Example 3). FIG. 9A is a subject-specific BNApattern constructed from a subject who declared that the pain wasrelatively high, and FIG. 9B is a subject-specific BNA patternconstructed from a subject who declared that the pain was relativelylow. As shown, the difference in pain level is expressed in the BNApatterns, wherein for subjects experiencing low pain the size of the BNApattern is smaller than for subjects experiencing high pain. Thus, thesize of the BNA pattern can be used as an indicator for the level ofpain.

In some embodiments of the present invention BNA pattern 20 is comparedto a BNA pattern constructed for the same subjects at a different time.These embodiments are useful for many applications.

For example, in some embodiments, the comparison is used for determiningpresence, absence and/or level of neural plasticity in the brain.

Brain plasticity relates to the ability of the brain to adapt(functionally and/or structurally) to changed conditions, sometimesafter injury or strokes, but more commonly in acquiring new skills.Brain plasticity has been demonstrated in many basic tasks, withevidence pointing to physical modifications in the cortex during torepetitive performance. The plasticity of neural interactions resultingfrom repetitive performance of specific tasks is known to lead toimproved performance.

Determination of neural plasticity is particularly useful for subjectssuffering a stroke, wherein part of the brain is damaged and other partsbegin to function or change their function. A comparison between twoBNA's of a subject after a stroke can be used to identify a change inbrain activity hence also to assess neural plasticity in the brain. Insome embodiments of the present invention a late stage BNA pattern isconstructed for a subject during the subject's rehabilitation. A latestage BNA pattern is optionally from data acquired during severalrehabilitation sessions, preferably at a sufficiently advanced stage ofthe rehabilitation. Such BNA pattern can be viewed as a neural networkpathway achieved by the brain in order to overcome motor dysfunction. Asubject-specific BNA pattern, constructed during an individual sessioncan then be compared to the late stage BNA pattern, thereby establishinga learning curve for the subject.

Determination of neural plasticity is particularly useful for subjectssuffering from chronic pain. It is recognized by the present inventorsthat, the presence of chronic pain is perceived and established in thebrain, and is oftentimes accompanied by chemical changes in the brain.For example, there is a decrease in N-acetyl aspartate and changes inother brain metabolites. The chemical changes result in depression,anxiety and/or a loss of cognitive memory functions. A comparisonbetween two BNA's of the subject can be used to identify a change inbrain activity hence also to assess those chemical changes. Suchassessment can be used, for example, in combination with a painstimulus, to determine the likelihood that the subject is a chronic painsufferer or having normal response to the pain stimulus.

In some embodiments, a BNA pattern constructed from neurophysiologicaldata acquired following a treatment is compared to a BNA patternconstructed from neurophysiological data acquired before a treatment.Such comparison can be used for assessing responsiveness to andoptionally efficacy of the treatment. This can be done generally asdescribed above with respect to FIGS. 6A-D, 7A-D and 8A-D, except thatthe comparisons are between two BNA patterns of the same subject insteadof between a BNA pattern of the subject and a baseline BNA pattern of agroup.

In some embodiments, a BNA pattern constructed from neurophysiologicaldata acquired while the subject performs a particular task is comparedto a BNA pattern constructed from neurophysiological data acquired whilethe subject is not performing the particular task and/or while thesubject performs another particular task. A representative example forthese embodiments will now be described with reference to FIGS. 10A-H.

FIGS. 10A-H show group BNA patterns constructed from EEG data recordedfrom two groups of subjects during a working memory test. The black dotsin FIGS. 10A-H show the locations of the EEG electrodes, and the colorcodes in these BNA patterns is the same as defined above. During thetest, each subject of the group was asked to memorize an image of ahuman face (referred to as the “cue”). Two seconds later, the subjectwas again presented with an image of a human face (referred to as the“probe”) and was asked to determine whether the probe matches the cue.

The BNA patterns of the first group are shown in FIGS. 10A-D. FIGS. 10Aand 10B are group BNA patterns constructed following treatment with aplacebo (referred to below as placebo A), and FIGS. 10C and 10D aregroup BNA patterns constructed following treatment with a Scopolamine.The BNA patterns of the second group are shown in FIGS. 10E-H, whereFIGS. 10E and 10F are group BNA patterns constructed following treatmentwith a placebo (referred to below as placebo B), and FIGS. 10G and 10Hare BNA patterns constructed following treatment with a Ketamine.

The effect of scopolamine is explained above. Ketamine is widelyrecognized as a general nonbarbiturate anesthetic that acts quickly toproduce an anesthetic state. More specifically, ketamine is anacrylcycloalkylamine used traditionally in the induction of dissociativeanesthesia. Ketamine has been used to induce anesthesia prior toelective surgery in healthy children, and also to induce anesthesia inelderly subjects who could not tolerate general anesthesia.

The BNA pattern of FIGS. 10A, 10C, 10E and 10G were constructed from thedata acquired during the time at which the cue was presented and arerecognized by the present inventor as containing information pertainingto the memorization process in the brain (also known in the literatureas “encoding”). The BNA patterns of FIGS. 10B, 10D, 10F and 10H wereconstructed from the data acquired during the time at which the probewas presented, and are recognized by the present inventor as containingto information pertaining to the retrieval process in the brain. It isnoted that the BNA patterns of FIGS. 10A-H describe differentiatingactivity networks. Thus, for example, the BNA pattern of FIG. 10Adescribes the brain activity during cue that most differentiated betweenplacebo A and Scopolamine, and the BNA pattern of FIG. 10B describes thebrain activity during cue that most differentiated between placebo B andKetamine.

As shown in FIGS. 10A-B and 10E-F, following treatment with placebo, theBNA pattern during retrieval is substantially larger in both the orderand the size than the BNA pattern during memorization. The situation isdifferent following treatment with Scopolamine and Ketamine. Thescopolamine (FIGS. 10C-D) induced (i) low connectivity between frontaland parietal regions, and (ii) extensive compensatory central andfrontal activation. The ketamine (FIGS. 10G-H) induced increased centraland frontal activation, and decreased right lateralization. Nosignificant change in the fronto-parietal part of the BNA pattern wasobserved.

Further details regarding analysis of neurophysiological data acquiredfrom subjects administered with scopolamine are provided in the Examplessection that follows (see Example 4).

The BNA pattern comparison technique of the present embodiments can alsobe used for inducing improvement in brain function. In some embodimentsof the present invention associated subject-specific BNA patterns areconstructed for a subject during a higher-level cognitive test,generally in real time. The subject can be presented with theconstructed BNA patterns or some representation thereof and use them asa feedback. For example, when, as a result of the cognitive action, theBNA pattern of the subject becomes more similar to a characteristic BNApattern of a healthy group, presentation of such a result to the subjectcan be used by the subject as a positive feedback. Conversely, when, asa result of the cognitive action, the BNA pattern of the subject becomesmore similar to a characteristic BNA pattern of a brain-disorder group,presentation of such a result to the subject can be used by the subjectas a negative feedback. Real time analysis of BNA patterns inconjunction with neurofeedback can optionally and preferably be utilizedto achieve improved cortical stimulation using external stimulatingelectrodes.

The BNA pattern comparison technique of the present embodiments can alsobe to used for assessing responsiveness to and optionally efficacy of aphototherapy. Phototherapy is the application of light energy tobiological tissue for the purpose of stimulating certain biologicalfunctions, such as natural tissue healing and regrowth processes.Alternatively, a higher power level of phototherapy may inhibit naturalbiological functions of the tissue or destroy the tissue, as may beapplied in the case of cancerous tissue.

Generally, phototherapy is accomplished by radiating light energy into asubject's tissue at or below the skin or surface of the tissue. Theradiation is applied at wavelengths either in the visible range or theinvisible infrared (IR) range. Phototherapy may also be accomplished byapplying coherent and non-coherent light energy, lased and non-lasedlight energy, and narrow and broadband light energy, in either acontinuous or pulsed manner. The radiation energy is also typicallyapplied at a low power intensity, typically measured in milliwatts. Therelatively low radiation energy applied in therapy is called low levellight therapy (LLLT). LLLT has also been suggested for neurologicaldisorders in the CNS, for the prevention and/or repair of damage, reliefof symptoms, slowing of disease progression, and correction of geneticabnormalities. In particular, phototherapy can be used following acerebrovascular accident (stroke).

The present embodiments can be used for assessing the responsiveness toand optionally the efficacy of phototherapy, particularly LLLT ofneurological disorders. Such assessment can be done by constructing BNApatterns from neurophysiological data acquired before, after andoptionally during phototherapy and comparing those BNA patterns amongthemselves and/or to baseline BNA pattern as further detailedhereinabove.

The BNA pattern comparison technique of the present embodiments can alsobe used for assessing responsiveness to and optionally efficacy ofhyperbaric therapy. Hyperbaric therapy is indicated for many medicalconditions, therapeutic purposes, and training regimens. Hyperbarictreatment can aid in the treatment of many oxygen dependent diseases aswell as sports injuries. Some of the ailments that can be effectivelytreated by hyperbaric therapy include: cerebral edema, traumatic headand spinal cord injury, chronic stroke, post stroke, early organic brainsyndrome, brain stem to syndromes, brain ischemia, brain bloodcirculation disturbances and headache disorder. Typically, treatment ina hyperbaric chamber is provided by administering oxygen to the user viaa closed-circuit mask, hood, or other device while a hyperbaric chamberis maintained at pressures above ambient pressure. The oxygen issupplied to the user from a supply source external to the chamber. Thesubject exhales through a closed system back outside the chamber suchthat the ambient air in the chamber remains less than 23.5% oxygen or isnot oxygen enriched. The environment within the chamber is alsogenerally maintained by a source external to the chamber and isgenerally controlled by a thermostat.

Assessment of responsiveness to and/or efficacy of hyperbaric therapycan be done by constructing BNA patterns from neurophysiological dataacquired before, after and optionally during hyperbaric therapy andcomparing those BNA patterns among themselves and/or to baseline BNApattern as further detailed hereinabove.

Additional examples of treatments which may be assessed by the BNApattern comparison technique of the present embodiments include, withoutlimitation, ultrasound treatment, rehabilitative treatment, and neuralfeedback, e.g., EMG biofeedback, EEG neurofeedback, transcranialmagnetic stimulation (TMS), and direct electrode stimulation (DES).

In some embodiments of the present invention local stimulation isapplied to the brain responsively to the information extracted from theBNA comparison. The local stimulation is optionally and preferably atone or more locations corresponding to a spatial location of at leastone of the nodes of the BNA pattern. Operations 51, 52 and 53 of themethod can be executed repeatedly, and the local stimulation can bevaried according to some embodiments of the present inventionresponsively to variations in the extracted information. Thus, thestimulation and BNA pattern analysis can be employed in a closed loop,wherein the BNA pattern analysis can provide indication regarding theeffectiveness of the treatment. The closed loop can be realized within asingle session with the subject, e.g., while the electrodes that areused to collect the data from the brain and the system that is used forapplying the stimulation engage the head of the subject.

The present embodiments contemplate many types of local stimulation.Representative examples including, without limitation, transcranialstimulation, to electrocortical stimulation on the cortex, and deepbrain stimulation (DBS).

Representative examples of transcranial stimulation techniques suitablefor the present embodiments include, without limitation, transcranialelectrical stimulation (tES) and transcranial magnetic stimulation(TMS). Representative examples of tES suitable for the presentembodiments include, without limitation, transcranial direct currentstimulation (tDCS), transcranial alternating current stimulation (tACS),and transcranial random noise stimulation (tRNS). tES can be eithermulti-focal or single focal. tES can be employed using any number ofelectrodes. Typically, the number of electrodes is from 1 to 256, butuse of more than 256 electrodes is also contemplated in some embodimentsof the present invention. In some embodiments of the present inventionhigh-definition tES (HD-tES), such as, but not limited to, HD-tDCS, isemployed.

tDCS and HD-tDCS suitable for the present embodiments are found forexample, in Edwards et al., Neurolmage 74 (2013) 266-275; Kuo et al.,Brain Stimulation, Volume 6, Issue 4 (2013) 644-648; and Villamar etal., J Pain. (2013) 14(4):371-83, the contents of which are herebyincorporated by reference.

The present embodiments also contemplate combining both transcranialstimulation and deep brain stimulation (DBS). These embodiments areuseful since the transcranial stimulation (e.g., tDCS or HD-tDCS) canimprove the effectiveness of DBS. In some embodiments the transcranialstimulation (e.g., tDCS or HD-tDCS) is executed before the DBS, whereinthe closed-loop with the BNA tern analysis is used for identifying theeffect of the stimulation on the brain. Once the effect is establishedDBS can be applied at locations at which the transcranial stimulation(e.g., tDCS or HD-tDCS) is effective (e.g., most effective).

In some embodiments of the present invention the transcranialstimulation (e.g., tDCS or HD-tDCS) is applied simultaneously orintermittently with the DBS. This improves the effectiveness of thetreatment by DBS. The combined stimulation (transcranial and DBS, e.g.,tES and DBS) can be achieved by means of the BNA pattern analysis of thepresent embodiments wherein regions on the BNA pattern that are far fromthe location of the DBS electrodes are stimulated transcarnially, andregions on the BNA pattern that are near the location of the DBSelectrodes are stimulated by the DBS electrodes. The combinedstimulation (transcranial and DBS, e.g., tES and DBS) can be employedfor activating and/or inhibiting activities in various regions in thebrain, as to manifested by the BNA pattern, either synchronously orindependently. In some exemplary embodiments of the invention thecombined stimulation (transcranial and DBS, e.g., tES and DBS) isemployed such that the transcranial stimulation (e.g., tDCS or HD-tDCS)is executed to control activation thresholds for the DBS. For example,the transcranial stimulation can lower the activation threshold at brainregions that are peripheral to the brain regions affected by DBS,thereby extending the effective range of the DBS. The transcranialstimulation can also increases the activation threshold at brain regionsaffected by DBS thereby controlling the stimulation path of the DBS.

DBS can optionally and preferably be employed to obtainneurophysiological data from the brain. These data can according to someembodiments of the present invention be used by the method to update theBNA pattern.

The local stimulation can be at one or more locations corresponding to aspatial location of at least one of the nodes of the BNA pattern. Forexample, the BNA pattern can be analyzed to identify locations thatcorrespond to a brain disorder. At these locations, local stimulationcan be applied to reduce or eliminate the disorder. Alternatively, thelocal stimulation can be applied at locations corresponding to othernodes of the BNA pattern. These other locations can be locations atwhich previous stimulations for the same subject or group of subjectshave been proven to be successful in reducing or eliminating thedisorder.

A representative example of application of local stimulation is in thecase of pain. In these embodiments the local stimulation is applied toreduce or eliminate the pain. Thus, the BNA pattern can be analyzed toidentify nodes that correspond to pain, and the stimulation can beapplied to locations that correspond to these nodes.

In some embodiments, a pain stimulus (such as heat stimulus) can beapplied to the subject prior to or while acquiring theneurophysiological data. The BNA pattern can be analyzed to identifynodes that correspond to the applied pain stimulus and the localstimulation can be at one or more locations corresponding to thoseidentified nodes. These embodiments are useful particularly, but notexclusively, for situations of chronic pain (e.g., fibromyalgia).

Aside for MPH, scopolamine and ketamine described above, the BNA patterncomparison technique can be used for assessing responsiveness to andoptionally efficacy of many other types of pharmacological treatments.

For example, when the subject suffers from a neurodegenerative disordersuch as Alzheimer's disease, the treatment can include use ofpharmacologically active agent selected from the group consisting ofdonepezil, physostigmine, tacrine, pharmaceutically acceptable acidaddition salts thereof, and combinations of any of the foregoing; whenthe subject suffers from a neurodegenerative disorder such asHuntington's disease, the treatment can include use of pharmacologicallyactive agent selected from the group consisting of fluoxetine,carbamazepine, and pharmaceutically acceptable acid addition salts andcombinations thereof; when the subject suffers from a neurodegenerativedisorder such as Parkinson's disease, the treatment can include use ofpharmacologically active agent selected from the group consisting ofamantadine, apomorphine, bromocriptine, levodopa, pergolide, ropinirole,selegiline, trihexyphenidyl, atropine, scopolamine, glycopyrrolate,pharmaceutically acceptable acid addition salts thereof, andcombinations of any of the foregoing; and when the subject suffers froma neurodegenerative disorder such as amyotrophic lateral sclerosis (ALS)the treatment can include use of pharmacologically active agent selectedfrom the group consisting of baclofen, diazepam, tizanidine, dantrolene,pharmaceutically acceptable acid addition salts thereof, andcombinations of any of the foregoing.

Generally, pharmacological treatments can include use of apharmacologically active agent, e.g., centrally acting drugs,particularly CNS active agents and other nervous system agents,including, but not limited to, the following: sympathomimetic amines;neuroprotective and neuroregenerative agents, including neurotrophicfactors; neuroactive amino acids and peptides; neurotransmitters;muscarinic receptor agonists and antagonists; anticholinesterases;neuromuscular blocking agents; ganglionic stimulating drugs; agents totreat neurodegenerative disorders such as Alzheimer's disease,Huntington's disease, Parkinson's disease, and amyotrophic lateralsclerosis (ALS); anti-epileptic agents; CNS and respiratory stimulants;and drugs that selectively modify CNS function, including anestheticagents, analgesic agents, antiemetic agents, antihypertensive agents,cerebral vasodilators, hypnotic agents and sedatives, anxiolytics andtranquilizers, neuroleptic agents, anti-microbial agents, alphaadrenergic receptor antagonists, and appetite suppressants. Some agents,as will be appreciated by those of ordinary skill in the art, areencompassed by two or more of the aforementioned groups.

Examples of these pharmacologically active agents include, withoutlimitation, sympathomimetic amines (e.g., include albuterol,amphetamine, benzphetamine, colterol, diethylpropion, dopamine, dopaminehydrochloride, dobutamine, ephedrine, epinephrine, epinephrinebitartrate, ethylnorepinephrine, ethylnorepinephrine hydrochloride,fenfluramine, fenoldopam, fenoldopam, fenoldopam mesylate,hydroxyamphetamine, hydroxyamphetamine hydrobromide, ibopamine,isoetharine, isoproterenol, isoproterenol hydrochloride, mephentermine,mephentermine sulfate, metaproterenol, metaraminol, metaraminolbitartrate, methoxamine, methoxamine hydrochloride, midodrine,norepinephrine, norepinephrine bitartrate, phendimetrazine,phenmetrazine, phentermine, phenylephrine, phenylephrine hydrochloride,phenylethylamine, phenylpropanolamine, prenalterol, propylhexedrine,ritodrine, terbutaline, terbutaline sulfate, and tyramine);Neuroprotective and neuroregenerative agents (e.g., excitatory aminoacid antagonists and neurotrophic factors, e.g., brain derivedneurotrophic factor, ciliary neurotrophic factor, and nerve growthfactor, neurotrophin(NT) 3 (NT3), NT4 and NT5); Neuroactive amino acidsand peptides (e.g., γ-aminobutyric acid (GABA), glycine, β-alanine,taurine, and glutamate, and the neuroactive peptides include bradykinin,kallidin, des-Arg.sup.9-bradykinin, des-Arg.sup.10-kallidin,des-Arg.sup.9-[Leu.sup.8]-bradykinin, [D-Phe.sup.7]-bradykinin, HOE 140,neuropeptide Y, enkaphalins and related opioid peptides such asMet.sup.5-enkaphalin, Leu.sup.5-enkephalin, α-, β- and γ-endorphin, α-and β-neo-endorphin, and dynorphin; neurotransmitters (e.g., GABA(γ-aminobutyric acid), glycine, glutamate, acetylcholine, dopamine,epinephrine, 5-hydroxytryptamine, substance P, serotonin, enkaphalinsand related opioid peptides as above, and catecholamines; Muscarinicreceptor agonists and antagonists (e.g., choline esters such asacetylcholine, methacholine, carbachol, bethanechol(carbamylmethylcholine), bethanechol chloride; cholinomimetic naturalalkaloids and synthetic analogs thereof, including arecoline,pilocarpine, muscarine, McN-A-343, and oxotremorine. Muscarinic receptorantagonists are generally belladonna alkaloids or semisynthetic orsynthetic analogs thereof, such as atropine, scopolamine, homatropine,homatropine methylbromide, ipratropium, methantheline, methscopolamineand tiotropium; anticholinesterases (e.g., ambenonium, ambenoniumchloride, demecarium, demecarium bromide, echothiophate iodide,edrophonium, edrophonium chloride, neostigmine, neostigmine bromide,neostigmine methylsulfate, physostigmine, physostigmine salicylate,pyridostigmine, to and pyridostigmine bromide); neuromuscular blockingagents and ganglionic blocking drugs (e.g., dicholine esters (e.g.,succinylcholine), benzylisoquinolines (d-tubocurarine, atracurium,doxacurium, mivacurium) and pipecuronium, rocuronium, vecuronium),hexamethonium, trimethaphan, and mecamylamine; agents to treatneurodegenerative diseases (e.g., active agents for treating Alzheimer'sdisease, such as Donezepil, donepezil hydrochloride, physostigmine,physostigmine salicylate, tacrine and tacrine hydrochloride, activeagents for treating Huntington's Disease such as, but not limited to,fluoxetine and carbamazepine, anti-Parkinsonism drugs such as, but notlimited to, amantadine, apomorphine, bromocriptine, levodopa(particularly a levodopa/carbidopa combination), pergolide, ropinirole,selegiline, trihexyphenidyl, trihexyphenidyl hydrochloride, andanticholinergic agents; and agents for treating ALS such as, but notlimited to, spasmolytic (anti-spastic) agents, e.g., baclofen, diazepam,tizanidine, and dantrolene); anti-epileptic agents (e.g.,anti-convulsant (anti-seizure) drugs such as azetazolamide,carbamazepine, clonazepam, clorazepate, ethosuximide, ethotoin,felbamate, gabapentin, lamotrigine, mephenytoin, mephobarbital,phenytoin, phenobarbital, primidone, trimethadione, vigabatrin, and thebenzodiazepines which are useful for a number of indications, includinganxiety, insomnia, and nausea); and CNS and respiratory stimulants(e.g., xanthines such as caffeine and theophylline; amphetamines such asamphetamine, benzphetamine hydrochloride, dextroamphetamine,dextroamphetamine sulfate, levamphetamine, levamphetamine hydrochloride,methamphetamine, and methamphetamine hydrochloride; and miscellaneousstimulants such as methylphenidate, methylphenidate hydrochloride,modafinil, pemoline, sibutramine, and sibutramine hydrochloride).

Also contemplated are drugs that selectively modify CNS function. Theseinclude, without limitation, anesthetic agents such as ketamine; opioidanalgesics such as alfentanil, buprenorphine, butorphanol, codeine,drocode, fentanyl, hydrocodone, hydromorphone, levorphanol, meperidine,methadone, morphine, nalbuphine, oxycodone, oxymorphone, pentazocine,propoxyphene, sufentanil, and tramadol; nonopioid analgesics such asapazone, etodolac, diphenpyramide, indomethacine, meclofenamate,mefenamic acid, oxaprozin, phenylbutazone, piroxicam, and tolmetin;antiemetics such as chlorpromazine, cisapride, domperidone, granisetron,metoclopramide, ondansetron, perphenazine, prochlorperazine,promethazine, thiethylperazine, and triflupromazine; antihypertensiveagents such as apraclonidine, clonidine, guanfacine, and guanabenz;cerebral vasodilators such as vincamine, naftidrofuryl oxalate,papaverine, and nicotinic acid; hypnotic agents and sedatives such asclomethiazole, ethinamate, etomidate, glutethimide, meprobamate,methyprylon, zolpidem, and barbiturates (e.g., amobarbital,apropbarbital, butabarbital, butalbital, mephobarbital, methohexital,pentobarbital, phenobarbital, secobarbital, thiopental); anxiolytics andtranquilizers such as benzodiazepines (e.g., alprazolam, brotizolam,chlordiazepoxide, clobazam, clonazepam, clorazepate, demoxepam,diazepam, estazolam, flumazenil, flurazepam, halazepam, lorazepam,midazolam, nitrazepam, nordazepam, oxazepam, prazepam, quazepam,temazepam, triazolam), buspirone, and droperidol; neuroleptic agents,including antidepressant drugs, antimanic drugs, and antipsychoticagents, wherein antidepressant drugs include (a) the tricyclicantidepressants such as amoxapine, amitriptyline, clomipramine,desipramine, doxepin, imipramine, maprotiline, nortryptiline,protryptiline, and trimipramine, (b) the serotonin reuptake inhibitorscitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, andvenlafaxine, (c) monoamine oxidase inhibitors such as phenelzine,tranylcypromine, and (−)-selegiline, and (d) other, “atypical”antidepressants such as bupropion, nefazodone, and trazodonevenlafaxine, and antimanic and antipsychotic agents include (a)phenothiazines such as acetophenazine, acetophenazine maleate,chlorpromazine, chlorpromazine hydrochloride, fluphenazine, fluphenazinehydrochloride, fluphenazine enanthate, fluphenazine decanoate,mesoridazine, mesoridazine besylate, perphenazine, thioridazine,thioridazine hydrochloride, trifluoperazine, and trifluoperazinehydrochloride, (b) thioxanthenes such as chlorprothixene, thiothixene,and thiothixene hydrochloride, and (c) other heterocyclic drugs such ascarbamazepine, clozapine, droperidol, haloperidol, haloperidoldecanoate, loxapine succinate, molindone, molindone hydrochloride,olanzapine, pimozide, quetiapine, risperidone, and sertindole;anticholinergic drugs such as atropine, scopolamine and glycopyrrolate;anti-microbial agents such as (a) tetracycline antibiotics and relatedcompounds (chlortetracycline, oxytetracycline, demeclocycline,methacycline, doxycycline, rolitetracycline), (b) macrolide antibioticssuch as erythromycin, clarithromycin, and azithromycin, (c)streptogramin antibiotics such as quinupristin and dalfopristin, (d)beta-lactam antibiotics, including penicillins (e.g., penicillin G,penicillin VK), antistaphylococcal penicillins (e.g., cloxacillin,dicloxacillin, nafcillin, and oxacillin), extended spectrum penicillins(e.g., aminopenicillins such as ampicillin and amoxicillin, and theantipseudomonal penicillins such as carbenicillin), and cephalosporins(e.g., cefadroxil, cefepime, cephalexin, cefazolin, cefoxitin,cefotetan, cefuroxime, cefotaxime, ceftazidime, and ceftriazone), andcarbapenems such as imiprenem, meropenem and aztreonam, (e)aminoglycoside antibiotics such as streptomycin, gentamicin, tobramycin,amikacin, and neomycin, (f) glycopeptide antibiotics such as vancomycin,and teicoplanin; (g) sulfonamide antibiotics such as sulfacetamide,sulfabenzamide, sulfadiazine, sulfadoxine, sulfamerazine,sulfamethazine, sulfamethizole, and sulfamethoxazole, (h) quinoloneantibiotics such as ciprofloxacin, nalidixic acid, and ofloxacin; (i)anti-mycobacterials such as isoniazid, rifampin, rifabutin, ethambutol,pyrazinamide, ethionamide, aminosalicylic, and cycloserine, (j) systemicantifungal agents such as itraconazole, ketoconazole, fluconazole, andamphotericin B, (k) antiviral agents such as acyclovir, famcicylovir,ganciclovir, idoxuridine, sorivudine, trifluridine, valacyclovir,vidarabine, didanosine, stavudine, zalcitabine, zidovudine, amantadine,interferon alpha, ribavirin and rimantadine, and (1) miscellaneousantimicrobial agents such as chloramphenicol, spectinomycin, polymyxin B(colistin), and bacitracin; alpha adrenergic receptor antagonists suchas doxazosin, indoramine, phenoxybenzamine, phentolamine, prazosin,tolazoline, terazosin, trimazosin, and yohimbine; and appetitesuppressants such as amphetamine, dextroamphetamine, dextroamphetaminesulfate, diethylpropion hydrochloride, mazindol, methamphetaminehydrochloride, phentermine, and phentennine hydrochloride.

FIG. 14 is a flowchart diagram illustrating a method suitable forconstructing a database from neurophysiological data recorded from agroup of subjects, according to some embodiments of the presentinvention.

The neurophysiological data to be analyzed can be any data acquireddirectly from the brain of the subject under investigation, as furtherdetailed hereinabove. The data can be analyzed immediately afteracquisition (“online analysis”), or it can be recorded and stored andthereafter analyzed (“offline analysis”). The neurophysiological datacan include any of the data types described above. In some embodimentsof the present invention the data are EEG data. The neurophysiologicaldata can be collected to before and/or after the subject has performedor conceptualized a task and/or action, as further detailed hereinabove.The neurophysiological data can be used as event related measures, suchas ERPs or ERFs, as further detailed hereinabove.

The method begins at 140 and optionally and preferably continues to 141at which the neurophysiological data are received. The data can berecorded directly from the subject or it can be received from anexternal source, such as a computer readable memory medium on which thedata are stored.

The method continues to 142 at which relations between features of thedata are determined so as to identify activity-related features. Theactivity-related features can be extrema (peaks, through, etc.) and theycan be identified as further detailed hereinabove.

The method continues to 143 at which a parcellation procedure isemployed according to the identified activity-related features so as todefine a plurality of capsules, each representing at least aspatiotemporal activity region in the brain. Broadly speaking,parcellation procedure defines a neighborhood of each identifiedfeature. The neighborhood is optionally and preferably a spatiotemporalneighborhood. In some embodiments of the present invention theneighborhood is a spectral-spatiotemporal neighborhood, theseembodiments are detailed hereinafter.

The neighborhood can be defined as a spatial region (two- orthree-dimensional) in which the extremum is located and/or atime-interval during which the extremum occurs. Preferably, both aspatial region and time-interval are defined, so as to associate aspatiotemporal neighborhood for each extremum. The advantage of definingsuch neighborhoods is that they provide information regarding thespreading structure of the data over time and/or space. The size of theneighborhood (in terms of the respective dimension) can be determinedbased on the property of the extremum. For example, in some embodiments,the size of the neighborhood equals the full width at half maximum(FWHM) of the extremum. Other definitions of the neighborhood are notexcluded from the scope of the present invention.

In various exemplary embodiments of the invention a spatial grid isbuilt over a plurality of grid elements. The input to the spatial gridbuilt is preferably the locations of the measuring devices (e.g.,locations on the scalp, epicortical surface, cerebral cortex or deeperin the brain). In various exemplary embodiments of the invention apiecewise to interpolation is employed so as to build a spatial gridhaving a resolution which is higher than the resolution characterizingthe locations of the measuring devices. The piecewise interpolationpreferably utilizes a smooth analytical function or a set of smoothanalytical functions.

In some embodiments of the present invention the spatial grid is atwo-dimensional spatial grid. For example, the spatial grid can describethe scalp, or an epicortical surface or an intracranial surface of thesubject.

In some embodiments of the present invention the spatial grid is athree-dimensional spatial grid. For example, the spatial grid candescribe an intracranial volume of the subject.

Once the spatial grid is built, each identified activity-related featureis preferably associated with a grid element x (x can be surface elementor a point location in embodiments in which a 2D grid is built, or avolume element or a point location in embodiments in which a 3D grid isbuilt) and a time point t. A capsule corresponding to the identifiedactivity-related feature can then be defined as a spatiotemporalactivity region encapsulating grid elements nearby the associated gridelement x and time points nearby the associated time point t. In theseembodiments, the dimensionality of a particular capsule is D+1, where Dis the spatial dimensionality.

The nearby grid elements optionally and preferably comprise all the gridelements at which an amplitude level of the respective activity-relatedfeature is within a predetermined threshold range (for example, abovehalf of the amplitude at the peak). The nearby time points optionallyand preferably comprise all time points at which the amplitude level ofthe activity-related feature is within a predetermined threshold range,which can be the same threshold range used for defining the nearby gridelements.

The parceling 143 can optionally and preferably includes applyingfrequency decomposition to the data to provide a plurality of frequencybands, including, without limitation, delta band, theta band, alphaband, low beta band, beta band, and high beta band, as further detailedhereinabove. Higher frequency bands, such as, but not limited to, gammaband are also contemplated. In these embodiments, the capsules can bedefined separately for each frequency band.

The present inventors also contemplate a parceling procedure in whicheach to identified activity-related feature is associated with afrequency value f, wherein the capsule corresponding to an identifiedactivity-related feature is defined as spectral-spatiotemporal activityregion encapsulating grid elements nearby x, time points nearby t, andfrequency values nearby f. Thus, in these embodiments, thedimensionality of a particular capsule is D+2, where D is the spatialdimensionality.

The definition of capsules according to some embodiments of the presentinvention is executed separately for each subject. In these embodiments,the data used for defining the capsules for a particular subjectincludes only the data collected from that particular subject,irrespective of data collected from other subjects in the group.

In various exemplary embodiments of the invention the method continuesto 144 at which the data are clustered according to the capsules, toprovide a set of capsule clusters. When the capsules are definedseparately for each frequency band, the clustering is preferably alsoexecuted separately for each frequency band. The input for theclustering procedure can include some or all the capsules of allsubjects in the group. A set of constraints is preferably defined,either a priori or dynamically during the execution of the clusteringprocedure, which set of constraints is selected to provide a set ofclusters each representing a brain activity event which is common to allmembers of the cluster. For example, the set of constraints can includea maximal allowed events (e.g., one or two or three) per subject in acluster. The set of constraints can also include a maximal allowedtemporal window and maximal allowed spatial distance in a cluster. Arepresentative example of a clustering procedure suitable for thepresent embodiments is provided in the Examples section that follows.

Once the clusters are defined, they can optionally and preferably beprocessed to provide a reduced representation of the clusters. Forexample, in some embodiments of the present invention a capsularrepresentation of the clusters is employed. In these embodiments, eachcluster is represented as a single capsule whose characteristicsapproximate the characteristics of the capsules that are the members ofthat cluster.

In some embodiments, the method proceeds to 145 at which inter-capsulerelations among capsules are determined. This can be done using theprocedure described above with respect to the determination of the edgesof the BNA pattern (see, for example, FIGS. 3B-E). Specifically, theinter-capsule relations can represent causal to relation between twocapsules. For example, for each of a given pair of capsules, a timewindow can be defined. These time windows correspond to the width of thecapsule along the time axis. A latency difference window between the twocapsules can also be defined. This latency difference window correspondsto the separation along the time axis between the capsules.

The individual time windows and latency difference window can be used todefine the relation between the pair of capsules. For example, athreshold procedure can be applied to each of these windows, so as toaccept, reject or quantify (e.g., assign weight to) a relation betweenthe capsules. The threshold procedure can be the same for all windows,or, more preferably, it can be specific to each type of window. Forexample, one threshold procedure can be employed to the width of thecapsule along the time axis, and another threshold procedure can beemployed to the latency difference window. The parameters of thethresholding are optionally dependent on the spatial distance betweenthe capsules, wherein for shorter distance lower time thresholds areemployed.

The present embodiments contemplate many types of inter-capsulerelations, including, without limitation, spatial proximity between twodefined capsules, temporal proximity between two defined capsules,spectral (e.g., frequency of signal) proximity between two definedcapsules, and energetic (e.g., power or amplitude of signal) proximitybetween two defined capsules.

In some embodiments, a group capsule is defined for a group of subjectseach having capsule and spatiotemporal peak. The relation between twogroup capsules is optionally and preferably defined based on the timedifference between the respective group capsules. This time differenceis preferably calculated between the corresponding two spatiotemporalpeaks of subjects from both group capsules. This time difference canalternatively be calculated between the onsets of the spatiotemporalevent activations of each of the capsules (rather than the timedifferences between peaks).

For example, the two group capsules can be declared as a pair of relatedcapsules if the time difference between the capsules among subjectshaving those capsules is within a predefined time window. This criterionis referred to as the time-window constraint. A typical time-windowsuitable for the present embodiments is several milliseconds.

In some embodiments, the relation between two group capsules is definedbased on the number of subjects having time those capsules. For example,the two group capsules can be declared as a pair of related capsules ifthe number of subjects having the capsules is above a predeterminedthreshold. This criterion is referred to as the subject numberconstraint. In various exemplary embodiments of the invention the bothtime window constraint and the subject number constraint are used inaddition, wherein two group capsules are declared as a pair of relatedcapsules when both the time window constraint and the subject numberconstraint are fulfilled. The maximum number of subjects that can createa particular pair of capsules is referred to as the intersection ofsubjects of the two groups.

Thus, in the present embodiments a capsule network pattern isconstructed, which capsule network pattern can be represented as a graphhaving nodes corresponding to capsules and edges corresponding tointer-capsule relations.

In some embodiments of the present invention the method applies(operation 149) a feature selection procedure to the capsules to provideat least one sub-set of capsules.

In some embodiments of the present invention a forward selection offeatures is employed and in some embodiments of the present invention abackward selection features is employed. In some embodiments of thepresent invention the method employs a procedure for controlling thefraction of false positives that may lead to poor selection, suchprocedure is known as false discovery rate (FDR) procedure, and isfound, for example, in Benjamini et al. supra, the contents of which arehereby incorporated by reference.

A representative example of a feature selection procedure suitable forthe present embodiments is illustrated in FIG. 33. Initially, a group ofsubjects is considered (for example, either healthy controls or diseasedsubjects), optionally and preferably using a sufficiently large datasetto as to provide relatively high accuracy in representing the group. Thegroup can be represented using a set of capsules. The feature selectionprocedure is then applied on a training set of the dataset in order toevaluate each feature or various combinations of features characterizingthe group's dataset. The input to the feature selection algorithm ispreferably evaluation scores (e.g., the score for each participant inthe training set on each of the features) calculated using the trainingset. Feature selection can also be applied, on other features, such as,but not limited to, BNA pattern event-pairs, and EEG and ERP featuressuch as, but not limited to, coherence, correlation, timing andamplitude measures. Feature selection can also be applied on differentcombinations of these features.

The outcome of this procedure can be a set of supervised network ofcapsules, each suitable to describe a different sub-group of thepopulation with a specific set of features. The networks obtained duringthe procedure can allow a comparison of the capsules obtained for asingle subject to a specific network or networks. Thus, the obtainednetworks obtained can serve as biomarkers.

In some embodiments of the invention, the method continues to 146 atwhich weights are defined for each cluster (or capsular representationthereof) and/or each pair of clusters (or capsular representationsthereof). Weights for pairs of clusters can be calculated as describedabove with respect to the weights assigned to the edges of the BNA.

Weights for individual capsules or clusters can describe the existencelevel of the particular capsule in the database. For example, the weightof a cluster can be defined as the mean amplitude as calculated over allthe capsules in the cluster. The weight is optionally and preferablynormalized by the sum of all amplitude means of all clusters.

Also contemplated is a weight that describes the statisticaldistribution or density of one or more of the parameters that define thecapsules in the cluster. Specifically, the weight can include at leastone of: the distribution or density of the amplitudes over the cluster,the spatial distribution or spatial density over the cluster, thetemporal distribution or temporal density over the cluster, and thespectral distribution or spectral density over the cluster.

At 147 the method stores the clusters and/or representations and/orcapsule network pattern in a computer readable medium. When weights arecalculated, they are also stored.

The method ends at 148.

FIG. 15 is a flowchart diagram illustrating a method suitable foranalyzing neurophysiological data recorded from a subject, according tosome embodiments of the present invention.

The neurophysiological data to be analyzed can be any data acquireddirectly from the brain of the subject under investigation, as furtherdetailed hereinabove. The data can be analyzed immediately afteracquisition (“online analysis”), or it can be recorded and stored andthereafter analyzed (“offline analysis”). The neurophysiological datacan include any of the data types described above. In some embodimentsof the present invention the data are EEG data. The neurophysiologicaldata can be collected before and/or after the subject has performed orconceptualized a task and/or action, as further detailed hereinabove.The neurophysiological data can be used as event related measures, suchas ERPs or ERFs, as further detailed hereinabove.

The method begins at 150 and optionally and preferably continues to 151at which the neurophysiological data are received. The data can berecorded directly from the subject or it can be received from anexternal source, such as a computer readable memory medium on which thedata are stored.

The method continues to 152 at which relations between features of thedata are determined so as to identify activity-related features. Theactivity-related features can be extrema (peaks, through, etc.) and theycan be identified as further detailed hereinabove.

The method continues to 153 at which a parcellation procedure isemployed according to the identified activity-related features so as todefine a plurality of capsules, as further detailed hereinabove. Thecapsules and the relations between capsules define a capsule networkpattern of the subject, as further detailed hereinabove.

In some embodiments, the method proceeds to 157 at which a featureselection procedure is employed as further detailed hereinabove.

The method optionally and preferably continues to 154 at which adatabase having a plurality of entries, each having an annotateddatabase capsule is accessed. The database can be constructed asdescribed above with respect to FIG. 14.

The term “annotated capsule” refers to a capsule which is associatedwith annotation information. The annotation information can be storedseparately from the capsule (e.g., in a separate file on a computerreadable medium). The annotation information can be associated with asingle capsule or a collection of capsules. Thus, for example, theannotation information can pertain to the presence, absence or level ofthe specific disorder or condition or brain function. Also contemplatedare embodiments in which the annotation information pertains to aspecific brain related disorder or condition in relation to a treatmentapplied to the subject. For example, a capsule (or collection ofcapsules) can be annotated as corresponding to a treated brain relateddisorder. Such capsule (or collection of capsules) can also be annotatedwith the characteristics of the treatment, including dosage, duration,and elapsed time following the treatment. A capsule (or collection ofcapsules) can optionally and preferably be annotated as corresponding toan untreated brain related disorder. Any of the disorders, conditionsbrain functions, and treatments described above can be included in theannotation information.

Alternatively or additionally, the capsule (or collection of capsules)can be identified as corresponding to a specific group of individuals(e.g., a specific gender, ethnic origin, age group, etc.), wherein theannotation information pertains to the characteristics of this group ofindividuals.

The database can include capsules defined using data acquired from agroup of subjects, or it can capsules defined using data acquired fromthe same subject at a different time, for example, an earlier time. Inthe latter case, the annotation of the capsules can include theacquisition date instead or in addition to the aforementioned types ofannotations.

The method proceeds to 155 at which at least some (e.g., all) of thedefined capsules are compared to one or more reference capsules.

The present embodiments contemplate more than one type of referencecapsules.

In some embodiments of the present invention the reference capsules arebaseline capsules defined using neurophysiological data acquired fromthe same subject at a different time, for example, an earlier time.

A particular and non limiting example for these embodiments is the caseof several treatment sessions, e.g., N sessions, for the same subject.Data can be acquired before and after each session and capsules can bedefined for each data acquisition. The capsules defined before treatmentcan be used as baseline capsules to which capsules acquired from posttreatment acquisition can be compared. In some embodiments of thepresent invention the baseline capsules are capsules defined fromacquisition before the to first session, wherein capsules defined fromeach successive acquisition are compared to the same baseline capsules.This embodiment is useful for assessing the effect of the treatment overtime. In some embodiments of the present invention the baseline capsulesare capsules defined from acquisition before the kth session, whereincapsules defined from an acquisition following the kth session arecompared to these baseline capsules. This embodiment is useful forassessing the effect of one or more particular sessions.

The comparison can optionally be used for determining presence, absenceand/or level of neural plasticity in the brain.

Determination of neural plasticity is particularly useful for subjectssuffering a stroke, wherein part of the brain is damaged and other partsbegin to function or change their function. A comparison between twocapsules or set of capsules of a subject after a stroke can be used toidentify a change in brain activity hence also to assess neuralplasticity in the brain. Determination of neural plasticity isparticularly useful for subjects suffering from chronic pain. Acomparison between two capsules or set of capsules can be used toidentify a change in brain activity hence also to assess those chemicalchanges. Such assessment can be used, for example, in combination with apain stimulus, to determine the likelihood that the subject is a chronicpain sufferer or having normal response to the pain stimulus.

The comparison can optionally be used for of estimating the likelihoodof Traumatic brain injury (TBI). TBI is often classified into mild,moderate and severe TBI based on three parameters: 1) the quality andlength of change in consciousness, 2) the length of amnesia (memoryloss), and 3) the Glasgow Coma Scale (GCS). Traditionally, a braininjury is classified as concussion (mTBI) is the length of consciousnessis less than 20 minutes and amnesia is 24 hours or less, and is the GCSscore is above 13. A comparison between two capsules or set of capsulesof the same subject can be used to identify a change in brain activityhence also to assess the presence absence or likelihood of TBI, e.g.,brain concussion.

In some embodiments of the present invention the reference capsules arecapsules defined using neurophysiological data acquired form a differentsubject.

The variation of a particular capsule as defined from the data relativeto the baseline capsule (for example, as defined previously, or asdefined from previously to acquired data), can be compared according tosome embodiments of the present invention to variations among two ormore capsules annotated as normal. For example, the variation of aparticular capsule relative to the baseline capsule can be compared to avariation of a first capsule annotated as normal and a second capsulealso annotated as normal. These annotated capsules are optionally andpreferably defined from neurophysiological data acquired from differentsubjects identified as having normal brain function.

The advantage of these embodiments is that they allow assessing thediagnostic extent of the observed variations of a particular capsulerelative to a baseline capsule. For example, when the variation relativeto the baseline capsule are similar to the variations obtained fromneurophysiological data among two or more different subjects identifiedas having normal brain functions, the method can assess that theobserved variation relative to the baseline capsule are of reduced or nosignificance. On the other hand, when the variation relative to thebaseline capsule are substantive compared to the variations among normalsubjects, the method can assess that the observed variation relative tothe baseline capsule are diagnostically significant.

In embodiments in which a database of previously annotated capsules isaccessed (operation 154) the reference capsules are optionally andpreferably the capsules of the database. The capsules can be compared toat least one database capsule annotated as abnormal, and at least onedatabase capsule annotated as normal. A database capsule annotated asabnormal is a capsule which is associated with annotation informationpertaining to the presence, absence or level of a brain related disorderor condition. A database capsule annotated as normal is a capsule whichwas defined using data acquired from a subject or a group of subjectsidentified as having normal brain function. Comparison to a databasecapsule annotated as abnormal and a database capsule annotated as normalis useful for classifying the subject according to the respective brainrelated disorder or condition. Such classification is optionally andpreferably provided by means of likelihood values expressed usingsimilarities between the respective capsules.

The comparison between capsules is typically for the purpose ofdetermining similarity between the compared capsules. The similarity canbe based on correlation between the capsules along any number ofdimensions. In experiments performed by to the present inventors,correlation between two capsules that were not even in their size wasemployed. These experiments are described in more detail in the Examplessection that follows.

The comparison between capsules can comprise calculating a scoredescribing the degree of similarity between the defined capsule and thecapsules of the data base. When the database corresponds to a group ofsubjects having a common disorder, condition, brain function, treatment,or other characteristic (gender, ethnic origin, age group, etc.), thedegree of similarity can express, for example, the membership level ofthe subject in this group. In other words, the degree of similarityexpresses how close or how far are the disorder, condition, brainfunction, treatment, or other characteristic of the subject from that ofthe group.

The score calculation can include calculating of a statistical score(e.g., z-score) of a spatiotemporal vector corresponding to thesubject's capsule using multidimensional statistical distribution (e.g.,multidimensional normal distribution) describing the respective databasecapsule. In some embodiments of the present invention, the statisticalscore is weighed using the weights in the database. The scorecalculation can also include calculation of a correlation betweencapsule and a respective database capsule. A representative example of ascoring procedure suitable for the present embodiments is provided inthe Examples section that follows.

The score of a particular score relative to the database can also beused for comparing two capsules two each other. For example, consider afirst capsule C1 and a second capsule C2 which, a priori, is not thesame as C1. Suppose that C1 is compared to database X and is assignedwith a score S1. Suppose further that C2 is compared to a database Y(which, in some embodiments is database X, but may also be a differentdatabase) and is assigned with a score S2. The comparison between C1 andC2 is achieved according to some embodiments of the present invention bycomparing SI to S2. These embodiments are particularly useful when oneof C1 and C2 is a baseline capsule, and when C1 and C2 are defined fromneurophysiological data collected from different subjects.

The comparison between the subject's capsule and database capsules canbe executed irrespective of any inter-capsule relation of any type. Inthese embodiments the subject's capsule is compared to the databasecapsules without taking into account to whether a particular pair ofdatabase capsules has a relation in terms of time, space, frequency oramplitude.

Alternatively, the method can determine inter-capsule relations amongthe defined capsules, and construct a capsule network patternresponsively to the inter-capsule relations, as further detailedhereinabove. In these embodiments, the comparison is between theconstructed pattern and the database pattern.

The comparison between the subject's capsule and database capsules isoptionally and preferably with respect to the supervised network ofcapsules obtained during the feature selection procedure (see, forexample, FIG. 33).

Several comparison protocols are contemplated, and are schematicallyillustrated in FIGS. 34A-C.

In the comparison illustrated in FIG. 34A, a matching process thatallows quantifying the degree of similarity between the brain activityof the single subject and that represented by the network(s) isemployed. The overall degree of similarity can be quantified, accordingto some embodiments of the present invention, by a score which is aweighted sum of the separated similarity scores associated with all ofthe compared features. In embodiments in which several networks areobtained, each network characterizes a specific sub-group in thepopulation. In these embodiments, the subject can be matched against anetwork or networks associated with a sub-group that most resemble thecharacteristics of the subject.

In the comparison illustrated in FIG. 34B, the capsule network patternof the subject is compared against the group network and therepresentative matching features (e.g., best matching features) of thesingle subject to those of the group network are preferably selected.These representative matching features can be used as an approximationof the intersection between the single-subject capsule network and thegroup network and constitute a personalized single-subject sub-networkthat serves as a reference baseline used in multiple tests of the samesubject.

In some embodiments, the single subject may be compared against severalgroup sub-networks describing homogeneous subtypes enabling fine-tuningin choosing a single subject network that can serve as a reference.Thus, matching individual features to the features of the group'snetwork allows the extraction of a customized network and a comparisonof the individual to a sub-set of features most characterizing theircondition (e.g., healthy, diseased).

In the comparison illustrated in FIG. 34C, various combination ofcomparisons are shown. These include, but are not limited to, singlesubject network against another single subject network, network againstthe intersection between the network and the single subject network, andthe like.

The method ends at 156.

In various exemplary embodiments of the invention, the informationextracted from the comparison 155 pertains to the likelihood of abnormalbrain function for the subject. Additionally, the comparison canoptionally and preferably be used for extracting prognostic information.For example, the capsules can be compared to a reference (e.g.,baseline) set of capsules that characterizes a group of subject allsuffering from the same abnormal brain function with similarrehabilitation history, wherein the baseline set of capsules isconstructed from neurophysiological data acquired at the beginning ofthe rehabilitation process. The similarity level between the capsulesobtained at 153 and the reference set of capsules can be used as aprognosis indicator for the particular abnormal brain function and theparticular rehabilitation process.

The likelihood of abnormal brain function is optionally and preferablyextracted by determining a brain-disorder index based, at least in part,on the similarity between the capsules obtained at 153 and the referenceset of capsules, as further detailed hereinabove with respect to thecomparison of BNA pattern 20 and the annotated BNA pattern(s).

It is to be understood that the capsules of the present embodiments canbe used for assessing likelihood of many brain related disorders,including any of the aforementioned brain related disorders.

A baseline set of capsules can also be associated with annotationinformation pertaining to a specific brain related disorder or conditionof a group of subjects in relation to a treatment applied to thesubjects in the group. Such baseline set of capsules can also beannotated with the characteristics of the treatment, including dosage,duration, and elapsed time following the treatment. A comparison of thecapsules obtained at 153 to such type of baseline set of capsules canprovide information to regarding the responsiveness of the subject totreatment and/or the efficiency of the treatment for that particularsubject. Such comparison can optionally and preferably be used forextracting prognostic information in connection to the specifictreatment. A set of capsules that is complementary to such baseline setof capsules is a set of capsules that is annotated as corresponding toan untreated brain related disorder.

Optionally and preferably, the method compares the capsules obtained at153 to at least one baseline set of capsules annotated as correspondingto a treated brain related disorder, and at least one baseline set ofcapsules annotated as corresponding to an untreated brain relateddisorder.

The capsules of the present embodiments can also be used for determininga recommended dose for the subject. Specifically, the dose can be varieduntil a sufficiently high or maximal similarity to the baseline set ofcapsules for treated subjects is obtained. Once such similarity isachieved, the method can determine that the dose achieving suchsimilarity is the recommended dose for this subject.

In various exemplary embodiments of the invention, the comparisonbetween capsules is used to extract information pertaining to the levelof pain the subject is experiencing. Preferably, the informationincludes an objective pain level. Pain level assessment according tosome embodiments of the present invention is particularly useful ininstitutions that provide treatment or rehabilitation for subjectssuffering from chronic pain.

In some embodiments of the present invention the capsules obtained at153 are compared to a set of capsules constructed for the same subjectsat a different time. These embodiments are useful for many applications.

For example, in some embodiments, the comparison is used for determiningpresence, absence and/or level of neural plasticity in the brain, asfurther detailed hereinabove with respect to the comparison between BNApatterns.

In some embodiments, a set of capsules obtained from neurophysiologicaldata acquired following a treatment is compared to a set of capsulesobtained before a treatment. Such comparison can be used for assessingresponsiveness to and optionally efficacy of the treatment.

In some embodiments, a set of capsules obtained from neurophysiologicaldata acquired while the subject performs a particular task is comparedto a set of capsules to obtained from neurophysiological data acquiredwhile the subject is not performing the particular task and/or while thesubject performs another particular task.

The capsules of the present embodiments can also be used for inducingimprovement in brain function. In some embodiments of the presentinvention a set of capsules is obtained for a subject during ahigher-level cognitive test, generally in real time. The subject can bepresented with the set of capsules (for example, a graphicalpresentation can be used) and use them as a feedback. For example, when,as a result of the cognitive action, the set of capsules of the subjectbecomes more similar to a characteristic set of capsules of a healthygroup, presentation of such a result to the subject can be used by thesubject as a positive feedback. Conversely, when, as a result of thecognitive action, the set of capsules of the subject becomes moresimilar to a characteristic set of capsules of a brain-disorder group,presentation of such a result to the subject can be used by the subjectas a negative feedback. Real time analysis of BNA patterns inconjunction with neurofeedback can optionally and preferably be utilizedto achieve improved cortical stimulation using external stimulatingelectrodes.

The capsules of the present embodiments can also be used for assessingresponsiveness to and optionally efficacy of a phototherapy and/orhyperbaric therapy, as further detailed hereinabove with respect to thecomparison between BNA patterns.

Additional examples of treatments which may be assessed by the capsulescomparison technique of the present embodiments include, withoutlimitation, ultrasound treatment, rehabilitative treatment, and neuralfeedback, e.g., EMG biofeedback, EEG neurofeedback, transcranialmagnetic stimulation (TMS), and direct electrode stimulation (DES).

In some embodiments of the present invention local stimulation isapplied to the brain responsively to the information extracted from thecomparison 155. The local stimulation is optionally and preferably atone or more locations corresponding to a spatial location of at leastone of the nodes of the BNA pattern. Operations 151, 152 and 153 of themethod can be executed repeatedly, and the local stimulation can bevaried according to some embodiments of the present inventionresponsively to variations in the extracted information. Thus, thestimulation and capsule analysis can be employed in a closed loop,wherein the capsule analysis can provide indication regarding theeffectiveness of the treatment. The closed loop can be realized within asingle session to with the subject, e.g., while the electrodes that areused to collect the data from the brain and the system that is used forapplying the stimulation engage the head of the subject.

The present embodiments contemplate many types of local stimulation.Representative examples including, without limitation, transcranialstimulation, electrocortical stimulation on the cortex, and DBS.

Representative examples of transcranial stimulation techniques suitablefor the present embodiments include, without limitation, transcranialelectrical stimulation (tES) and transcranial magnetic stimulation(TMS). Representative examples of tES suitable for the presentembodiments include, without limitation, transcranial direct currentstimulation (tDCS), transcranial alternating current stimulation (tACS),and transcranial random noise stimulation (tRNS). tES can be eithermulti-focal or single focal. tES can be employed using any number ofelectrodes. Typically, the number of electrodes is from 1 to 256, butuse of more than 256 electrodes is also contemplated in some embodimentsof the present invention. In some embodiments of the present inventionhigh-definition tES (HD-tES), such as, but not limited to, HD-tDCS, isemployed.

The present embodiments also contemplate combining both transcranialstimulation and deep brain stimulation (DBS). These embodiments areuseful since the transcranial stimulation (e.g., tES, such as, but notlimited to, tDCS or HD-tDCS) can improve the effectiveness of DBS. Insome embodiments the transcranial stimulation (e.g., tDCS or HD-tDCS) isexecuted before the DBS, wherein the closed-loop with the capsuleanalysis is used for identifying the effect of the stimulation on thebrain. Once the effect is established DBS can be applied at locations atwhich the transcranial stimulation (e.g., tDCS or HD-tDCS) is effective(e.g., most effective).

In some embodiments of the present invention the transcranialstimulation (e.g., tDCS or HD-tDCS) is applied simultaneously orintermittently with the DBS. This improves the effectiveness of thetreatment by DBS. The combined stimulation (transcranial and DBS, e.g.,tES and DBS) can be achieved by means of the capsule analysis of thepresent embodiments wherein spatial regions of the set of capsules thatare far from the location of the DBS electrodes are stimulatedtranscarnially, and spatial regions of the set of capsules that are nearthe location of the DBS electrodes are stimulated by the DBS electrodes.The combined stimulation (transcranial and DBS, e.g., tES and DBS) canbe employed for activating and/or inhibiting activities in various toregions in the brain, as manifested by the obtained capsules, eithersynchronously or independently. In some exemplary embodiments of theinvention the combined stimulation (transcranial and DBS, e.g., tES andDBS) is employed such that the transcranial stimulation (e.g., tDCS orHD-tDCS) is executed to control activation thresholds for the DBS. Forexample, the transcranial stimulation can lower the activation thresholdat brain regions that are peripheral to the brain regions affected byDBS, thereby extending the effective range of the DBS. The transcranialstimulation can also increases the activation threshold at brain regionsaffected by DBS thereby controlling the stimulation path of the DBS.

DBS can optionally and preferably be employed to obtainneurophysiological data from the brain. These data can according to someembodiments of the present invention be used by the method to update theset of capsules.

The local stimulation can be at one or more locations corresponding to aspatial location of at least one of the capsules of the capsule networkpattern. For example, the capsule network pattern can be analyzed toidentify locations that correspond to a brain disorder. At theselocations, local stimulation can be applied to reduce or eliminate thedisorder. Alternatively, the local stimulation can be applied atlocations corresponding to other capsules of the capsule networkpattern. These other locations can be locations at which previousstimulations for the same subject or group of subjects have been provento be successful in reducing or eliminating the disorder.

A representative example of application of local stimulation is in thecase of pain. In these embodiments the local stimulation is applied toreduce or eliminate the pain. Thus, the capsule network pattern can beanalyzed to identify capsules that correspond to pain, and thestimulation can be applied to locations that correspond to thesecapsules.

In some embodiments, a pain stimulus (such as heat stimulus) can beapplied to the subject prior to or while acquiring theneurophysiological data. The capsule network pattern can be analyzed toidentify capsules that correspond to the applied pain stimulus and thelocal stimulation can be at one or more locations corresponding to thoseidentified capsules. These embodiments are useful, particularly, but notexclusively, for situations of chronic pain (e.g., fibromyalgia).

FIG. 32 is a schematic illustration of a system 320 for analyzing toneurophysiological data. System 320 comprises a data processor 322,e.g., a dedicated circuitry or a general purpose computer, configuredfor receiving the neurophysiological data, and executing at least someof the operations described herein. System 320 can comprise a sensingsystem 324 configured for sensing and/or recording theneurophysiological data and feeding data processor 322 with the data. Insome embodiments of the present invention the system comprises acontroller 326 connectable to a brain stimulation system 328. Controller326 is optionally and preferably configured for controlling brainstimulation system 328 to apply local stimulation to the brain (notshown) responsively to the estimated brain function. The brainstimulation system 328 can be of any type, including, withoutlimitation, transcranial stimulation system, tDCS system, HD-tDCSsystem, electrocortical stimulation system configured to applyelectrocortical stimulation on the cortex, DBS system, and the like.

As used herein the term “about” refers to ±10%.

The word “exemplary” is used herein to mean “serving as an example,instance or illustration.” Any embodiment described as “exemplary” isnot necessarily to be construed as preferred or advantageous over otherembodiments and/or to exclude the incorporation of features from otherembodiments.

The word “optionally” is used herein to mean “is provided in someembodiments and not provided in other embodiments.” Any particularembodiment of the invention may include a plurality of “optional”features unless such features conflict.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”.

The term “consisting of means “including and limited to”.

The term “consisting essentially of” means that the composition, methodor structure may include additional ingredients, steps and/or parts, butonly if the additional ingredients, steps and/or parts do not materiallyalter the basic and novel characteristics of the claimed composition,method or structure.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format to is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to includeany cited numeral (fractional or integral) within the indicated range.The phrases “ranging/ranges between” a first indicate number and asecond indicate number and “ranging/ranges from” a first indicate number“to” a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numerals therebetween.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Various embodiments and aspects of the present invention as delineatedhereinabove and as claimed in the claims section below find experimentalsupport in the following examples.

EXAMPLES

Reference is now made to the following examples, which together with theabove descriptions illustrate some embodiments of the invention in a nonlimiting fashion.

Example 1 Exemplified Spatio-Temporal Parcellation (STEP) Procedure

The STEP Procedure of the present embodiments parcels the full spatialand temporal dimensions of the ERP into a set of unitary events, forexample, extremum points and their surroundings. The challenge ofmatching two or more biological time series collected from differentsubjects derives from the accepted existence of common hidden functionalmicrostates and shifting times among subjects performing a common task.The parcellation definition of STEP allows matching different signalswithout distorting signal shape and time dependency. Thus, a pool ofmicrostate sets of group members can undergo clustering in order todefine and isolate group-common templates.

The STEP Procedure of the present embodiments translates the relevantspatial spread and temporal dynamics in a natural way into a set ofmicrostates, thereby addressing two drawbacks in conventionalspatiotemporal analysis methods: the constraint of using the entirespatiotemporal map as a global state and the loss of time dynamics inthe microstate.

Methods Subjects

Two groups of healthy right handed male and female subjects, from twodifferent centers, participated in the study. The first group included40 subjects (17 males) with an age range of 23-64 years from Ra'anana,Israel and the second group included 60 subjects (30 males) with an agerange of 15-24 years from Kansas, USA. All participants signed informedconsent forms for undergoing the procedures, which were approved by theEthics Committee of the respective centers.

Task and Data Acquisition

All subjects underwent an auditory oddball task. In the auditory oddballtarget detection test, the subjects were requested to respond toauditory target stimuli that occur infrequently and irregularly within aseries of standard stimuli. There were 600 trials of which 80% were 2000Hz stimuli (Frequent), 10% were 1000 Hz rare stimuli requiring aresponse (Target) and 10% were rare non-targets composed of varioussounds (Novel). Stimuli were separated by 1500 ms intervals.

Subjects were requested to fixate on a sign in the middle of a screen.Sound was delivered using a headset and the sound level was set to 70dB. Subjects of the first to group went through three repeated sessionsspaced one week apart.

EEG recordings were obtained using a 64-channel Biosemi Active Twosystem (Amsterdam, Netherlands). The sampling rate was 256 Hz. Thesecond group went through two repeated sessions and recordings wereobtained using a HydroCel Geodesic Sensor Net of 128-channels and netamps 300 amplifier of EGI (Eugene, Oreg.). The sampling rate was 250 Hz.

Artifact removal procedure included noisy electrode removal (extensiveranges of amplitude outside the range of ±100 μV or high dissimilarityto neighbor electrodes), noisy epoch removal (epochs with amplitudeoutside the range of ±100 μV or if a channel's amplitude deviated from 7STDs from its mean) and eye artifact correction using ICA. All artifactremoval stages were done using EEGLAB software (v. 9.0.4s).

Data Analysis

The data analysis procedure used in the present example according tosome embodiments of the invention is illustrated in the block diagram ofFIG. 15. The procedure included pre-processing, single subject featureextraction, group clustering and single subject scoring (relative togroup characteristics). Each of those stages can stand on its own,depending on different types of analysis.

For each subject, the ERPs were first decomposed into four conventionalfrequency bands, δ (0.5-4 Hz), θ (3-8 Hz), α (7-13 Hz) and β (12-30 Hz).Linear-phase FIR filter design using least-squares error minimizationand reverse digital filtering was used. Next, a high resolution spatialgrid of the brain activity (33*37 pixels) was calculated. For each timesample, the activity of all recording electrodes was interpolated to a2D grid according to the estimated projection of the 3D electrode arrayby use of cubic splines interpolation.

After pre-processing, the procedure extracted spatio-temporal events andtheir associated surroundings. A spatiotemporal event was defined as anextremum amplitude point (peak). The peaks surrounding was defined asall voxels around the peak (on the spatial 2D grid as well as on thetime dimension) with activity higher than half the amplitude absolutevalue of the peak. The ensuing features that characterized eachsubject's brain activity were sets of all peaks and of encapsulatedactivity regions in time and space around the peak for each frequencyband (FIG. 16, block B). These activity to regions are referred to inthis Example as capsules.

Block C represents a clustering operation in which the encapsulatedbrain activity regions were clustered for a group of subjects at a givenfrequency band. The input features for the clustering are all capsulesof all subjects in the group. The clustering goal was to get a set ofclusters, each representing a unitary event common to all members of thecluster.

In order to achieve this goal, a constraint of maximum participation ofone unitary event per subject in a cluster was applied. Additionalconstraints included a maximum temporal window and spatial distanceallowed in a cluster. The temporal windows were 200, 125, 77, 56 ms inaccordance with the four frequency bands of δ, θ, α and β, respectively.The spatial window was equivalent to the minimum distance betweennon-neighboring electrodes in the 10-20 system of 64 electrodes.

The clustering procedure contained 3 stages, as follows.

a. Map all optional clusters. The mapping was done under the constraintsspecified above and ignored clusters which were contained within higherquality clusters.

b. Implement a greedy procedure to choose the best clusters, in order tohave a set of clusters that contains at least 70% of the subjects andwithout any peaks overlapping between clusters. The quality measure of acluster was a factor of a combination of the number of participatingsubjects, the Euclidian distance between individual peaks and the peakssurroundings correlations in a cluster. A correlation between two peakssurroundings was calculated after normalizing the surroundings by theGlobal Field Power (GFP) as defined in Ref. [6] and alignment of thesurroundings by their peaks.

c. Get a group representation. A group representation has the samecharacteristics as a single subject representation. In the presentExample, the group representation was a set of capsules equal in numberto the number of clusters achieved by the previous stage. A group's peaklocation was defined as the average of the peak locations of all membersof the cluster. In order to arrive at a group's peak surroundings, anaveraged surrounding was calculated. For each subject participating inthe cluster, his original high resolution ERP was taken and aligned tothe group's peak by the relevant subject's peak. Averaging of allaligned ERPs provided a new averaged high resolution ERP around thegroup's peak, from which the surroundings of the peak were extracted.The surrounding was extracted in the same manner as in a single subject.The final output of the clustering was a set of group common capsules,which were averages of the single subjects' capsules contained in eachoriginal cluster. This set of capsules characterized the group-commonbrain activity.

At block D, single subject scoring was calculated relative to the set ofgroup-common capsules. A single subject representation was similar tothat of a group in terms of peaks and surroundings, except for the grouphaving means and SDs for the peaks locations. Naturally, a group hadless unitary events than a single subject. The subject score was aweighted sum of the best match of his capsules to those of the group:

$S_{score} = {\sum\limits_{S_{i},G_{j}}^{{{pairs}\mspace{14mu} {of}\mspace{14mu} {best}}{{matched}\mspace{14mu} {peaks}}}\left( {{capsule\_ corr}\left( {S_{i},G_{j}} \right)*{S_{temporal\_ dist}\left( {S_{i},G_{j}} \right)}*{G_{amp\_ weight}\left( G_{j} \right)}} \right)}$

where S_(i), G_(j) are the best matched pair of capsules found by thescoring algorithm of the single subject and group, respectively;capsule_corr(S_(i),G_(j)) equals zero if S_(peak)(i), G_(peak)(j) do notmeet the constraints, and con(S_(surr)(i), G_(surr)(j)) otherwise;S_(peak)(i), G_(peak)(j) are the spatio-temporal peaks of the singlesubject and the group, respectively; S_(surr)(i), G_(surr)(j) are thecapsules of the single subject and the group, respectively; corr(•,•) iscorrelation normalized and aligned to the peak correlation; S_(temporal)_(—) _(dist) is defined as:

S _(temporal) _(—) _(dist)(S _(i) ,G _(j))=N(S _(peak)(i);μ(G_(peak)(j)),σ²(G _(peak)(j)));

N(•; μ, σ²) is the normal distribution with μ, σ² parameters; G_(amp)_(—) _(weight) is defined as:

${{G_{amp\_ weight}\left( G_{j} \right)} = {{{mean}_{amp}\left( {G_{peak}(j)} \right)}/{\sum\limits_{k}^{allpeaks}{{mean}_{amp}\left( {G_{peak}(k)} \right)}}}};{and}$

mean_(amp)(•) is the mean of the amplitudes of the peaks in the cluster.

General Considerations

In lieu of a patient group for comparison with the normal one, theevoked response to the Novel stimulus was regarded as being apathological variant of the normal Target response and the ability ofthe STEP procedure to correctly classify the two responses was tested.

Additionally, in order to test the general applicability of the methodunder less rigorous and more realistic conditions, only the ERPs ofGroup 1 subjects during their to 3rd visit were used for creating thecommon templates. Members of Group 2 with its different age range,testing system, electrode number and placement and sampling rate werescored on those templates and the Target-Novel classification wasapplied on these members as well.

Results

Target group representation consisted of 15 capsules, 6 and 9 capsulesin the θ and α band, respectively. Novel group representation consistedof 14 capsules, 2, 5 and 7 in the δ, θ and α bands, respectively. Therelevant analysis time is 0 to 600 ms post-stimulus. Groups' capsulesare shown in FIGS. 17A and 17B. Shown in FIGS. 17A and 17B, are thecontour of the capsules of Target and Novel from the first group's 3rdvisit. The Y-Z plane is the 2D brain activity grid, and the points inthe middle of each capsule are the peaks.

The STEP procedure utilized in the present example successfullyclassified the Novel vs. Target responses. Clustering was performed onthe first group's 3rd visit. The other 2 visits of the first group andthe two visits of the second group were then classified against theensuing group capsules, based on STEP scoring.

ROC curves were calculated for all group, visit and frequency bandcombinations. It became apparent that 0 band capsules were dominant inall combined-frequency scores and that they are better Novel-Targetclassifiers then other frequency bands (Table 1). The respectivesensitivity and specificity values as derived from the cut-off points onthe θ band. ROC curve were 0.85 and 0.9 for the best classification(AUC=0.947) and 0.73 and 0.65 for the worst (AUC=0.77).

TABLE 1 ROC curves AUC values for all frequencies. Combined Group Visitδ θ A frequencies 1 1 0.697 0.871 0.721 0.872 2 0.716 0.947 0.736 0.9232 1 0.563 0.770 0.551 0.72  2 0.700 0.820 0.567 0.814

The θ band ROC curves for the 4 group-visit combinations are plotted inFIG. 18. In FIG. 18, G1 and G2 denote group 1 and group 2, respectively,and V1 and V2 denote the first and second visits, respectively. The bluecircles are cut-off points of the ROC analysis. The associatedstatistical details of the ROC curves shown in FIG. 18 are listed inTable 2.

TABLE 2 ROC statistical details Group Visit AUC SE P-value CISensitivity Specificity 1 1 0.871 0.041 <10⁻¹⁵ [0.79, 0.95] 0.85 0.75 20.947 0.026 <10⁻¹⁵ [0.89, 1.00] 0.85 0.90 2 1 0.770 0.045 9.4 × 10⁻¹⁰[0.68, 0.85] 0.73 0.65 2 0.820 0.041 1.5 × 10⁻¹⁵ [0.74, 0.89] 0.76 0.75

Discussion

The STEP procedure utilized in algorithm produced stimulus-specificgroup activity templates. The procedure correctly classified closelyrelated evoked responses. An improvement in classification can beachieved by locating and basing the score on capsules that show highdifferentiation characteristics.

Once the subject's ERP is satisfactorily represented, more than oneevaluation modes can be employed to assess change (mainly deterioration)in the ERP over time. In some embodiments, a wide as possible baselinedatabase is collect from the subject, against which each additionalperformance is optionally and preferably tested for conformity. In someembodiments of the present invention the widest common denominator inthe response of a representative group of normal subjects is defined,and the evolution of the single subject's conformity to that of thegroup is followed. The inventive STEP procedure is useful in theseembodiments since it allows grading the similarity between any twotrials as well as between a single trial and a derived group-commontemplate.

Example 2

Following is a description of a technique suitable for variousapplications, including, without limitation, concussion management,according to some embodiments of the present invention. In someembodiments, the technique is demonstrated on a scopolamine inducedcognitive impairment model.

The BNA technique of the present embodiments can be used to providequantitative and/or qualitative outputs that are useful according tosome embodiments of the present invention for monitoring brain activityof individual subjects over time.

The present inventors performed computer simulations and experimentsdirected to determine test-retest repeatability of the technique of thepresent embodiments, and for demonstrating the clinical applicationsoffered by the technique of the present embodiments.

Methods BNA Analysis

FIG. 19 is a block diagram describing the technique of the presentembodiments. The Reference Brain Network Model is generated to serve asa reference baseline integrated into the computerized method of thepresent embodiments and used to calculate BNA Scores of individualsubjects (layer 1). 40 healthy control subjects (18 males, 22 females)ages 23-64 were utilized for this purpose.

The Normative Database represents the change in BNA Scores (ΔBNA Scores)and is generated in order to determine the standard deviation (SEM) ofBNA Scores over repeat test sessions to establish a reference for trendanalysis (layer 2). 60 healthy control subjects (30 males, 30 females)ages 15-24 were utilized for this purpose. The trend analysis included asearch for a best trend over a plurality of trend candidates.

SEM cut-offs allow the clinician to estimate the degree of the relativechanges of the BNA Scores over time for trend analysis of theelectrophysiological brain activity (layer 3).

A BNA Analysis System generates according to some embodiments of thepresent invention quantitative scores from EEG data by comparing EEGactivity of a group of normative subjects to a set of reference brainnetwork models (Layer 1). These score can then be used to construct anormative database which typically constitutes at least these scores.The database can be utilized to determine statistical deviations (Layer2). BNA score of individual subjects can then be compared to thisdatabase, to provide a to tool for the assessment of trend analysis ofelectrophysiological changes over time (Layer 3).

The BNA Analysis of the present embodiments can be used for RevealingBNA patterns in groups of subjects, and/or for comparing brain activityof individual subjects to group BNAs. The comparison can include aqualitative output in the form of, for example, individual BNA patterns,and/or a quantitative output, in the form of, for example, one or more(e.g., 2, 3, 4 or more) BNA scores.

A group BNA analysis can provide a Reference Brain Network Model (see,Layer 1 in FIG. 19). A quantitative individual subject analysis canprovide a normative database (Layer 2 in FIG. 19). A quantitative and/orqualitative individual subject analysis can provide a trend analysis(Layer 3 in FIG. 19).

FIG. 20 illustrates an outline of a functional network analysis,suitable for the present embodiments. In some embodiments of the presentinvention the BNA analysis comprises two independent processes: grouppattern analysis (blue arrows) and individual subject evaluation (redarrows).

For the group analysis, the raw data (such as, but not limited to, EEGdata) of each subject undergoes at least one of the following processingstages: (1) preprocessing (A-C—artifact removal, band-passing); (2)salient event extraction (D-E—discretization, normalization), and (3)network analysis (F-H—clustering, unitary events extraction,pair-pattern extraction, group-template formation) performed on thepooled salient events of all subjects (multiple blue arrows). The stages(1)-(3) are optionally and preferably executed consecutively.

For the single subject level process, at least one of the followingstages is preferably executed: a first and a second stage can beidentical to those of the group level process (B-E), and a third stage,can include comparing the single subject activity to the set oftemplates issuing from the group analysis stage. The comparisonoptionally and preferably includes also calculating one or more scoresdescribing the comparison.

Cognitive Task

The task chosen for this study was the Auditory Oddball Task, but othertasks are not excluded from the scope of the present invention. FIG. 21is a schematic representation of an Auditory Oddball Task used in thisstudy. The task included 600 to repetitive 1000 ms auditory stimulationsof which 80% were 2000 Hz stimuli (Frequent—blue circles), 10% were 1000Hz stimuli requiring a motor response (Target—red circles) and 10% wererare non-targets composed of various sounds (Novel—yellow star). Thestimuli were separated by 1500 ms intervals. The sound was deliveredthrough a headset at a sound pressure level of 70 dB.

Statistical Analysis

-   -   Repeatability: Within-subject BNA score repeatability was        assessed by computing the Intraclass Correlation.    -   Normality Tests: Normality of ΔBNA distributions was evaluated        using the Kolmogorov-Smimov test of normality (p≧0.200) and        validated with corresponding Q-Q plots.    -   SEM Computation: The change over time in the BNA Scores of a        subject/patient from baseline (ΔBNA) is evaluated in terms of        standard error of measurements (SEM) against the Normative        Database to determine likelihood of true change.

Results Statistical Analysis of Normative Database

FIG. 22 shows normative Database's Interclass Correlation (ICC) valuesfor BNA scores in the two EEG-ERP sessions.

FIG. 23 shows Q-Q plot for the Connectivity ΔBNA scores of the Novelstimulus. The near-perfect linearity of the scattergram is strongevidence for normality.

FIG. 24 shows frequency histogram for the Connectivity ΔBNA scores ofthe Novel stimulus. Frequency units are number of scores out of 60. Oneand two SEMs around the mean are shown by the red lines.

Repeatability of Normative Database:

for all 12 BNA scores, the means of Visit 1 and Visit 2 were notsignificantly different. ICC values ranged from 0.47 to 0.83, with anaverage of 0.71 (SD=0.10), see FIG. 22.

Normal Distribution of ΔBNA Scores in a Nonnative Database:

The BNA Analysis System's normative database includes ΔBNA scores fromthe two consecutive EEG-ERP sessions. The normative database's ΔBNAscores were found to adhere to a to Gaussian distribution for all 12combinations of stimuli and scores, as inferred from the histograms andthe Q-Q plots (FIGS. 23 and 24).

Determination of SEM:

The computed 2SEM ranged from 16.04 to 40.62 (average=26.9) BNA scorepoints. As the Normative Database ΔBNA scores follow a Gaussiandistribution, it may be concluded with about 95% certainty that ΔBNAscores which fall outside of the 2SEM range are not a result of randomvariation (FIG. 24).

Clinical Applications Part A: Simulation

A subject was randomly selected from the normative database. The Targetand Novel stimuli were then manipulated by gradually attenuatingamplitude and increasing latency. This effectively simulated changesthat can occur in a variety of clinical conditions. A Multi-channelMatching Pursuit was then utilized at all 64 simulated scalp locations.

The resulting changes in BNA Scores following successive manipulationsteps were then calculated and evaluated against SEM values derived fromthe normative database.

FIG. 25 shows a reconstructed ERP at Fz channel of a randomly chosenhealthy subject from the normative database following a 6-step gradedmanipulation (combined amplitude decline and latency delay) of the P300component in response to the Novel stimulus. The top curve is theoriginal non-manipulated trace.

The simulation results are shown in FIGS. 26A-B. FIG. 26A shows plots of4 ΔBNA score values (Manipulation Level BNA score−baseline BNA score) asa function of the extent of manipulation. The boxed areas designate±1SEM and ±2SEM thresholds derived from the nonnative BNA database. FIG.26B shows the dependence of individual qualitative maps on the degree ofmanipulation. Red dots on group template designate scalp locationsinvolved in event-pairs (joined by lines). Red dots on individual mapsdesignates event-pairs common to the group template, the thickness ofjoining lines denoting how close the match is in terms of amplitude andtiming

As shown in FIGS. 26A-B, the individual qualitative maps becameincreasingly less complex and less resembling of the group template asmanipulation progressed. Accordingly, the ΔBNA scores were generallyshown to diminish in line with the extent of manipulation.

Part B—Cognitive Impairment Model

A pharmacological model study included 13 healthy volunteers of bothgenders, Aged 18-45. The volunteers were subjected to 3 Consecutive BNAsessions were, 1 week apart. A first session was used as a baseline, oneof the two other sessions included administration of scopolamine (0.4mg), and the other of the two other sessions included administration ofplacebo. The second and third sessions were at random order, doubleblind. ΔBNA Score values (Baseline−Placebo and Baseline−Drug) wereevaluated against SEM values.

FIG. 27 shows pharmacological model results. Shown are Plots of the ΔBNA(Baseline−treatment) connectivity score values for the Novel response.Each symbol is a single subject, tested once following Drug and oncefollowing Placebo. Horizontal lines are ±1, 1.5 & 2SEM thresholds,derived from the normative BNA database.

The Results are presented for the Novel stimulus. Novelty processing isknown to be particularly vulnerable to cognitive decline. Eight out of13 patients receiving scopolamine had post-treatment BNA scores morethan 2 SEM lower than the baseline score, as compared to only 2 patientsreceiving placebo.

Conclusions

The present example demonstrates that the BNA technique of the presentembodiments has a high test-retest repeatability. The present exampledemonstrates that the BNA technique of the present embodiments can beutilized to follow clinically meaningful changes in brain activity ofindividual subjects. The present example demonstrates that a change inBNA Scores from baseline over time, as calculated in accordance withsome embodiments of the present invention can aid for monitoring diseasestates, particularly for concussion management.

Example 3 Exemplified Experimental Study for Sport-Related Concussion

This experimental study evaluated the efficacy of BNA analysis of thepresent embodiments to discriminate between concussed and non-concussedathletes over multiple time periods.

Little is known about changes in brain activity and connectivityfollowing a sport-related concussion. Event Related Potentials (ERPs),which are temporal reflections of electrophysiological response tostimuli, may provide valuable insight to the pathophysiological eventsthat underlie concussion. The BNA pattern of the present embodiments canbe utilized for identifying and optionally tracking the recoveryfollowing sport-related concussion.

A schematic flowchart of the employed technique is illustrated in FIG.28. High density EEG data are collected while the subject performedspecific computerized cognitive tasks. The EEG data are then processedaccording to some embodiments of the present invention and a set ofspatio-temporal activity patterns representing the activated brainnetworks is extracted. The single subject patterns are scored against atask matched and age matched Reference Brain Network (N=>90) to generatea BNA score.

Methodology Participants and Procedures

Participants comprised 35 concussed patients and 19 control athletes.University IRB was obtained prior to study. All athletes underwentcomputerized neurocognitive testing, symptom assessment, andelectrophysiological (EEG/ERP) assessment while performing threecognitive tasks: 1) Auditory Oddball, 2) Visual GoNoGo, and 3) SternbergMemory; within 10 days, 11-17 days, 18-24 days, and 25-31 dayspost-concussion.

Data Analysis

Brain networks associated with sport-related concussion were firstidentified. Interclass Correlation (ICC) values were calculated toevaluate the stability of the BNA scores in healthy controls across allpost-concussion visits. The ability to discriminate the brain networkactivity between concussed athletes and matched controls was evaluatedwith a Receiver Operating Characteristic (ROC) analysis.

Results

The BNA corresponding to the “GO” event in the GoNogo task and the BNAcorresponding to the “Frequent” event in the Auditory Oddball taskdemonstrated the best ability to discriminate between concussed athletesand matched controls. FIG. 29A shows the selected reference BNA for theGo/NoGo task, and FIG. 29B shows the selected reference BNA for theAuditory Oddball task.

The BNA scores related to the temporal unfolding of the network activity(absolute timing from stimuli and relative timing ofelectrophysiological events) were most sensitive to the concussiveeffect. FIG. 30 shows the reference BNA patterns used to generate BNAScores.

The InterClass Correlation (ICC) for the control groups revealed a highlevel of repeatability demonstrating stability of the BNA scores forthis group across four visits. ICC scores ranged from 0.66-0.69.

FIGS. 30A-D show group average BNA scores (% similarity to the referenceBNA) across 4 visits for the concussed group (n=35) and the controlgroup (n=19). A significant segregation of BNA scores between theconcussed and control group were observed on visit 1 (within an averageof 7.7 days post concussion) and converge across the follow up visits.

The sensitivity and specificity for the BNA patterns are shown in FIGS.31A-D. ROC analysis demonstrated a high discrimination between concussedathletes and healthy controls. The sensitivity ranged from about 0.74 toabout 0.85 and the specificity ranged from about 0.58 to about 0.68 withAUC values ranging from about 0.70 to about 0.76.

Example 4 Exemplified Feature Selection Procedure

A feature selection procedure was applied according to some embodimentsof the present invention to reduce the dimensionality of a capsulenetwork pattern.

Methods Subjects

About 110 subjects (ages 14-24) were recruited from the following threecenters: York University, University of Pittsburgh Medical Center (UPMC)and Vince & Associates Clinical Research (Kansas, USA). For the purposeof the study the Auditory Oddball, Auditory Go/No-Go, and Sternbergtasks were used. The training of the feature selection was performed ondata from 35 concussed and 20 controls from UPMC.

Tasks

In the oddball task, three auditory stimuli were randomly presented in aprobabilistic fashion, at an average rate of 1 stimulus every 1.5 sec.About 80% of the stimuli were pure tones of 2000 Hz (“standard”), about10% of the stimuli were pure tones of 1000 Hz (“target”), and about 10%of the stimuli were environmental sounds (“novel”), such as telephoneringing or dog bark, different for each stimulus presentation. Thesubjects responded by pressing a button with his/her right index finger.

In the Auditory Go/No-Go task, for each trial, either a Go or a NoGostimulus was presented. The No-Go stimulus was relatively rare (about20% of the occurrences) in comparison to the Go stimulus. No-Go cuesrequired subjects to inhibit a prepared motoric act and Go cues were thestimuli to which subjects were asked to respond as quickly as possible.

In the Sternberg memory task, the subjects were presented with a memoryset which included several serially displayed stimuli. After a shortretention interval, a probe stimulus was presented. The subjects wereasked to press one key if the probe was present in the memory set andanother key otherwise (50% “yes”). Difficulty level was manipulated bythe number of stimuli in the memory set.

Data Analysis

Parcellation was applied to the activity related features to definecapsules as further detailed hereinabove. Feature selection was appliedto the capsules corresponding to all events of all subjects, to providegroup characteristics followed by single-subject scoring. An event wasdefined as an extremum point in the spatiotemporal amplitude space andits associated surroundings. The features that characterize eachsubject's brain activity were defined as the sets of all capsules (peaksand encapsulated activity regions in time and space around the peak).The features were sorted by the combined sum of the area under the curve(AUC) of a receiver operating characteristic (ROC) curve and IntraClass-Correlation (ICC) using a forward model.

In a second study, the training set was applied to examine repeatabilityand negative predictive value (NPV). The NPV was defined as a summarystatistic that describes the probability that subjects with a negativetest result do not have the disease and are correctly diagnosed. In thisstudy the features were sorted by the combined sum of the AUC and ICCvalues.

Results

FIG. 35 shows one example of extracted spatiotemporal peaks in differentfrequency bands for the No-Go stimulus.

The measures of differentiation (AUC) and repeatability (ICC) for eachstimulus in the three cognitive tasks ranged between about 0.7 to 0.9(except for the Target's ICC) and are given in Table 3, below.

TABLE 3 Stimulus AUC ICC Go 0.78 0.9  NoGo 0.8  0.73 Frequent 0.77 0.86Target 0.74 0.64 Novel 0.79 0.67 Sternberg stimulus 0.74 0.74

Differentiation is graphically displayed in FIGS. 36A-C for exemplarystimuli from the different tasks (blue line=healthy controls; redline=concussed). In FIGS. 36A-C, a clear separation is shown betweenconcussed and healthy controls in the first visit in all three stimulustypes. This separation was diminished in subsequent visits but was stillevident in the second visit in the Novel (FIG. 36A) and Sternberg (FIG.36C) stimuli.

For the second study, training for NPV allowed to extract capsulenetworks that identify an individual without concussion with highprecision. That is, the capsule networks accurately determined that anindividual with a negative test result based on the networks is indeednot concussed. The features achieved a good NPV score of 0.72.

It was found by the present inventors that capsule networks which arethe outcome of training for NPV can aide in decision making. Forexample, when the subject is an athlete diagnosed as having asport-related concussion, the capsule networks can aid in decidingwhether the athlete can return to sport activity.

Example 5 Exemplified Experimental Study for Analysis and Treatment ofPain

A study directed to the analysis and treatment of pain has beenconducted according to some embodiments of the present invention.

In the study, evoked potentials were obtained by applying heat stimuli.The evoked potentials are referred to as contact heat evoked potentials(CHEPs). Tactile stimulus was applied by PATHWAY-CHEPS sensoryevaluation system (Medoc Ltd., Ramat-Yishai Israel). The technique ofthe present embodiments was applied to generate BNA patterns beforeduring and after the application of heat stimuli. The study includedHD-tDCS treatment guided by the obtained BNA patterns. HD-tDCS wasapplied by Soterix 4×1 s (Soterix, New York, USA) using 2.0 mA ofcurrent.

The electrodes used for HD-tDCS were Ag/AgCl sintered ring electrodes(EL-TP-RNG Sintered; Stens Biofeedback Inc, San Rafael, Calif.). Theelectrodes were held in place by specially designed plastic casingsembedded in a modular EEG recording cap. The center electrode (anode)was placed over C3 (International 10/20 Electroencephalogram System),which corresponded approximately to the location of the primary motorcortex. Four return electrodes (cathode) were placed in a radius ofapproximately 7.5 cm from the center electrode to focus the stimulationunder the target area. Their locations corresponded roughly to Cz, F3,T7, and P3.

FIG. 37 shows a visual analog scale (VAS) used in the study, and FIG. 38shows the area at which heat stimulus was applied. FIG. 38 is ananterior view of a human subject on which several areas are indicated.In the present study the C5-C6 dermatome was stimulated, about 4 cm downfrom the antecubital fossa. Both high and low temperatures were used. Amap of the electrodes that were used to collect the neurophysiologicaldata is illustrated in FIG. 39.

FIG. 40 is a flowchart diagram describing the protocol used in thestudy. Pre-screening was performed by phone to determine eligibility.The target enrollment was 15 subjects. In the first visit (durationabout 1.5 hours), baseline assessment was performed without stimulation.In this visit a baseline BNA pattern was constructed according to someembodiments of the present invention for each subject, as well as forthe group of subjects. Data were collected using an electrode cap havingthe electrode map shown in FIG. 39.

In the following visits (duration about 0.5-1.5 hours per visit), heatstimulations were applied as illustrated in FIG. 38. Additionally,active anodal tDCS was applied once per visit. Non-responder assessmenthas been performed following the stimulation of the 11th, the 16th,21st, 24th and 27th visits. All subjects had follow-up visits eitherafter response or after the 27th visit (whichever came first).

The reported VAS as a function of the numerical pain scale is shown inFIG. 41, where the filled circles represent VAS after treatment withHD-tDCS. As shown, there is a correlation between the acute pain score(NPS) and chronic pain score VAS. This demonstrates that a treatmentguided by the acute pain can be effective also to reduce chronic pain.Thus, a pain stimulus (such as heat stimulus) can be applied to thesubject prior to or while acquiring the neurophysiological data. The BNApattern can be analyzed to identify nodes that correspond to the appliedpain stimulus and the local stimulation can be based on the identifynodes.

FIG. 42 shows the BNA score, the VAS and the quality of life ratingscale, before treatment (baseline), after the 5th treatment session andafter the 10th treatment session. As shown, the BNA score is indicativeof pain reduction and quality of life improvement.

FIG. 43 shows the changes in the BNA scores after the first visit. Eachsubject for which the BNA score was significantly increased, wasdeclared as “responder,” and each subject for which the BNA score wassignificantly reduced was defined as “non-responder.”

A representative Example of a subject declared as responder is shown inFIGS. 44A-44D, where FIG. 44A shows BNA score weighted by connectivity,FIG. 44B shows BNA score weighted by amplitude, FIG. 44C shows the BNAscore as a function of NPS at low temperature and FIG. 44D shows the BNAscore as a function of NPS at high temperature. The temperature for thehigh CHEPs was 52° C. (shown in red), and the temperatures for the lowCHEPs was 49° C. (shown in blue). As shown, there is a reduction in theBNA score both during treatment (see, e.g., visit 7) and betweentreatments (see, e.g., between visit 2 and visit 11). Thus, the BNApatterns becomes more different than the BNA pattern that ischaracteristic to pain. This demonstrates that the subject is responsiveto treatment, whereby the stimulation reduces the pain.

A representative Example of a subject declared as non-responder is shownin FIGS. 45A-45C, where FIG. 45A shows BNA score weighted byconnectivity, FIG. 45B shows the BNA score as a function of NPS at lowtemperature and FIG. 45C shows the BNA score as a function of NPS athigh temperature. The temperature for the high CHEPs was 47° C. (shownin red), and the temperature for the low CHEPs was 45° C. (shown inblue).

This example demonstrates that acute model of pain can be used to treatchronic pain. Acute pain is induced and the response is identified inthe brain (via the BNA pattern or capsule network pattern). FIG. 42shows that BNA change (and thus the electrophysiology behind acute pain)correlates to chronic pain measures (the VAS), and FIGS. 44C-D and 45B-Cshow the relation between BNA score and the NPS score (acute pain).

REFERENCE

-   [1] F. H. Duffy, “Topographic display of evoked potentials: clinical    applications of brain electrical activity mapping (BEAM)”, Annals of    the New York Academy of Science, vol. 388, pp. 183-96, June, 1982.-   [2] D. Lehmann, “Principles of spatial analysis”, in Methods of    Analysis of Brain Electrical and Magnetic Signals, A. S. Gevins, A.    Remond, Eds. Amsterdam: Elsevier, 1987. pp. 309-54.-   [3] K. J. Friston, J. T. Ashburner, S. J. Kiebel, T. E. Nichols    and W. D. Penny, “Statistical parametric mapping: the analysis of    functional brain images”, London: Academic Press, 2001.-   [4] K. E. Stephan, L. M. Harrison, S. J. Kiebel, O. David, W. D.    Penny and K. J. Friston, “Dynamic causal models of neural system    dynamics: current state and future extensions”, Journal of    Biosciences, vol. 32, no. 1, pp. 129-144, January 2007.-   [5] C. M. Michel, M, Seeck and T. Landis. “Spatio-temporal dynamics    of human cognition”, News in Physiological Sciences, vol. 14, pp.    206-214, October 1999.-   [6] D. Brunet, M. M. Murray and C. M. Michel, “Spatiotemporal    analysis of multichannel EEG: CARTOOL”, Computational Intelligence &    Neuroscience, vol. 2011, pp. 813-870, January 2011.-   [7] C. D. Woody, “Characterization of an adaptive filter for the    analysis of variable latency neuroelectric signals”, Medical &    Biological Engineering, vol. 5, no. 6, pp. 539-554, November 1967.-   [8] R. Bellman and R. Kalaba, “On adaptive control processes”, IRE    Trans on Automatic Control, vol. 4, no. 2 pp. 1-9, November 1959.-   [9] A. Efrat, Q. Fan and S. Venkatasubramanian, “Curve matching,    time warping and light fields: New algorithms for computing    similarity between curves”, Journal of Mathematical & Imaging    Visualization, vol. 27, no. 3, pp. 203-216, April 2007.-   [10] D. Comaniciu and P. Meer, “Mean Shift: A robust approach toward    feature space analysis”, IEEE Trans on Pattern Analysis and Machine    Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.-   [11] J. Polich, “Updating p300: An integrative theory of p3a and    p3b”, Clinical Neurophysiology, vol. 118, no. 10, pp. 2128-2148,    October 2007.-   [12] D. J. Linden, “The P300: Where in the Brain Is It Produced and    What Does It Tell. Us?” The Neuroscientist, vol. 11 no. 6 pp.    563-576, November 2005.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting.

1. A method of analyzing neurophysiological data recorded from a brainof a subject, the method being executed by a data processor andcomprising: identifying activity-related features in the data; parcelingthe data according to said activity-related features to define aplurality of capsules, each representing a spatiotemporal activityregion in the brain; comparing at least some of said defined capsules toat least one reference capsule; and estimating a brain function of thesubject based on said comparison.
 2. The method according to claim 1,wherein said comparison comprises calculating, for each of said at leastsome of said defined capsules, a statistical score of a spatiotemporalvector corresponding to said capsule using multidimensional statisticaldistribution describing a respective database capsule.
 3. The methodaccording to claim 2, wherein each entry of said database is alsoassociated with a weight, and the method further comprises weighing saidstatistical score using said weight.
 4. The method according to claim 2,further comprising calculating a correlation between said capsule and arespective database capsule.
 5. The method according to claim 1, whereinsaid comparison is executed irrespective of any inter-capsule relation.6. The method according to claim 5, wherein said inter-capsule relationcomprises at least one of spatial proximity between two definedcapsules, temporal proximity between two defined capsules, spectralproximity between two defined capsules, and energetic proximity betweentwo defined capsules.
 7. The method according to claim 1, furthercomprising determining inter-capsule relations among said capsules, andconstructing a capsule network pattern responsively to saidinter-capsule relations, wherein said database comprises databasecapsule network patterns, and where said comparison comprises comparingsaid constructed pattern to said database pattern.
 8. (canceled)
 9. Themethod according to claim 1, wherein said at least one reference capsulecomprises an annotated database capsule stored in a database having aplurality of entries, and the method further comprises accessing saiddatabase.
 10. The method according to claim 1, wherein said at least onereference capsule comprises a baseline capsule defined usingneurophysiological data acquired from the same subject at a differenttime, and the method comprises comparing the variation of said capsulerelative to said baseline capsule, to a previously stored variation of afirst capsule annotated as normal and a second capsule also annotated asnormal. 11-12. (canceled)
 13. The method according to claim 1, whereinsaid at least one reference capsule comprises a capsule defined usingneurophysiological data acquired form a different subject. 14.(canceled)
 15. The method according to claim 1, further comprising:constructing a brain network activity (BNA) pattern having a pluralityof nodes, each representing a feature of said activity-related features;assigning a connectivity weight to each pair of nodes in said BNApattern; comparing said constructed BNA to at least one reference BNApattern; wherein said estimation of said a brain function of the subjectis also based on said comparison to said reference BNA.
 16. The methodaccording to claim 15, wherein said at least one reference BNA patterncomprises an annotated BNA pattern stored in a BNA database having aplurality of entries, and the method further comprises accessing saiddatabase.
 17. The method according to claim 15, wherein said at leastone reference BNA pattern comprises a baseline BNA pattern extractedfrom neurophysiological data acquired from the same subject at adifferent time.
 18. The method according to claim 15, wherein said atleast one reference BNA pattern comprises a BNA pattern extracted fromneurophysiological data acquired form a different subject or a group ofsubjects.
 19. The method according to claim 1, further comprising, priorto said comparison, applying a feature selection procedure to saidplurality of capsules to provide at least one sub-set of capsules,wherein said comparison is executed separately for each of said at leastone sub-set of capsules.
 20. The method according to claim 1, whereinsaid brain function is a temporary abnormal brain function.
 21. Themethod according to claim 1, wherein said brain function is a chronicabnormal brain function.
 22. The method according to claim 1, whereinsaid brain function is a response to a stimulus or lack thereof.
 23. Themethod according to claim 1, comprising assessing the likelihood ofbrain concussion.
 24. The method according to claim 1, comprisingapplying local stimulation to the brain responsively to said estimatedbrain function, said local stimulation being at one or more locationscorresponding to a spatial location of at least one of said capsules.25. The method according to claim 1, comprising applying localstimulation to the brain responsively to said estimated brain function.26. The method according to claim 25, wherein said local stimulation isat one or more locations corresponding to a spatial location of at leastone of said capsules.
 27. The method according to claim 25, wherein saidestimation of said brain function is executed repeatedly, and the methodcomprises varying said local stimulation responsively to variations insaid brain function.
 28. The method according to claim 25, wherein saidlocal stimulation comprises transcranial stimulation.
 29. The methodaccording to claim 25, wherein said local stimulation comprisestranscranial direct current stimulation (tDCS).
 30. The method accordingto claim 25, wherein said local stimulation comprises high-definitiontranscranial direct current stimulation (HD-tDCS).
 31. The methodaccording to claim 25, wherein said local stimulation compriseselectrocortical stimulation on the cortex.
 32. The method according toclaim 25, wherein said local stimulation comprises deep brainstimulation.
 33. The method according to claim 25, wherein said localstimulation comprises both transcranial stimulation and deep brainstimulation, and wherein said transcranial stimulation is executed tocontrol activation thresholds for said deep brain stimulation.
 34. Themethod according to claim 24, wherein said local stimulation comprisesboth transcranial stimulation and deep brain stimulation, and whereinsaid transcranial stimulation is executed to control activationthresholds for said deep brain stimulation.
 35. A method of constructinga database from neurophysiological data recorded from a group ofsubjects, the method being executed by a data processor and comprising:identifying activity-related features in the data; parceling the dataaccording to said activity-related features to define a plurality ofcapsules, each representing a spatiotemporal activity region in thebrain; clustering the data according to said capsules, to provide aplurality of capsule clusters; and storing said clusters and/orrepresentations thereof in a computer readable medium, thereby formingthe database.
 36. The method according to claim 35, wherein saidrepresentations of said clusters comprises capsular representations ofsaid clusters.
 37. The method according to claim 35, further comprisingdetermining inter-capsule relations among said capsules, andconstructing capsule network patterns responsively to said inter-capsulerelations, wherein said representations of said clusters comprise saidcapsule network patterns.
 38. The method according to claim 35, whereinsaid parceling comprises forming a spatial grid, associating eachidentified activity-related feature with a grid element and a timepoint, and defining a capsule corresponding to said identifiedactivity-related feature as a spatiotemporal activity regionencapsulating grid elements nearby said associated grid element and timepoints nearby said associated time points. 39-41. (canceled)
 42. Themethod according to claim 38, wherein said spatial grid is atwo-dimensional spatial grid.
 43. The method according to claim 38,wherein said spatial grid is a two-dimensional spatial grid describing ascalp of the subject.
 44. The method according to claim 38, wherein saidspatial grid is a two-dimensional spatial grid describing anintracranial surface of the subject.
 45. The method according to claim38, wherein said spatial grid is a three-dimensional spatial grid. 46.The method according to claim 38, wherein said spatial grid is athree-dimensional spatial grid describing an intracranial volume of thesubject.
 47. The method according to claim 38, wherein said parcelingcomprises applying frequency decomposition to the data to provide aplurality of frequency bands, wherein said association of saididentified activity-related feature and said definition of said capsuleis executed separately for each frequency band.
 48. (canceled)
 49. Themethod according to claim 38, wherein said parceling comprisesassociating each identified activity-related feature with a frequencyvalue, and wherein said capsule corresponding to said identifiedactivity-related feature is defined as spectral-spatiotemporal activityregion encapsulating grid elements nearby said associated grid element,time points nearby said associated time points and frequency valuesnearby said associated frequency value.
 50. (canceled)
 51. A system forprocessing neurophysiological data, comprising a data processorconfigured for receiving the neurophysiological data, and executing themethod according to claim
 1. 52. A computer software product, comprisinga computer-readable medium in which program instructions are stored,which instructions, when read by a data processor, cause the dataprocessor to receive the neurophysiological data and execute the methodaccording to claim
 1. 53. A system for analyzing neurophysiological datarecorded from a brain of a subject, the system comprises a dataprocessor configured for: identifying activity-related features in thedata; parceling the data according to said activity-related features todefine a plurality of capsules, each representing a spatiotemporalactivity region in the brain; comparing at least some of said definedcapsules to at least one reference capsule; and estimating a brainfunction of the subject based on said comparison.
 54. The system ofclaim 53, further comprising a controller connectable to a brainstimulation system and configured for controlling said brain stimulationsystem to apply local stimulation to the brain responsively to saidestimated brain function.
 55. The system according to claim 54, whereinsaid controller is configured to control said brain stimulation systemto apply said local stimulation at one or more locations correspondingto a spatial location of at least one of said capsules.
 56. The systemaccording to claim 54, wherein said estimation of said brain function isexecuted repeatedly, and said controller is configured to vary saidlocal stimulation responsively to variations in said brain function. 57.The system according to claim 54, wherein said brain stimulation systemcomprises a transcranial stimulation system.
 58. The system according toclaim 54, wherein said brain stimulation system comprises a transcranialdirect current stimulation (tDCS) system.
 59. The system according toclaim 54, wherein said local stimulation comprises high-definitiontranscranial direct current stimulation (HD-tDCS).
 60. The systemaccording to claim 54, wherein said brain stimulation system comprisesan electrocortical stimulation system configured to applyelectrocortical stimulation on the cortex.
 61. The system according toclaim 54, wherein said brain stimulation system comprises a deep brainstimulation system.
 62. The system according to claim 54, wherein saidbrain stimulation system is configured to apply both transcranialstimulation and deep brain stimulation, and wherein said controller isconfigured to control said brain stimulation system to apply saidtranscranial stimulation to control activation thresholds for said deepbrain stimulation.